def test_attenuation_coefficient(self):
"""Test data is all at an air pressure of one standard atmosphere, 101.325 Pa."""
data = np.loadtxt(data_path() + 'absorption_coefficient.csv',
skiprows=1,
delimiter=',')
f = np.array([
50.0, 63.0, 80.0, 100.0, 125.0, 160.0, 200.0, 250.0, 315.0, 400.0,
500.0, 630.0, 800.0, 1000.0, 1250.0, 1600.0, 2000.0, 2500.0,
3150.0, 4000.0, 5000.0, 6300.0, 8000.0, 10000.0
])
for row in data:
temperature = 273.15 + row[0] # Degrees Celsius to Kelvin
relative_humidity = row[1]
alpha = row[
2:] / 1000.0 # Given in dB/km while we calculate in dB/m.
assert (f.shape == alpha.shape)
a = Atmosphere(temperature=temperature,
relative_humidity=relative_humidity)
calculated_alpha = a.attenuation_coefficient(f)
np.testing.assert_array_almost_equal(alpha,
calculated_alpha,
decimal=2)
def test_read_csv_cirrus_details(filename):
from acoustics.aio import read_csv_cirrus
file = "_".join(filename) + ".csv"
csv_path = os.path.join(data_path(), "cirrus", file)
data = read_csv_cirrus(csv_path)
if filename[0] == "det_global":
np.sum(data.LAeq)
else:
np.sum(data["125Hz"])
def test_read_csv_cirrus_details(filename):
from acoustics.aio import read_csv_cirrus
file = "_".join(filename) + ".csv"
csv_path = os.path.join(data_path(), "cirrus", file)
data = read_csv_cirrus(csv_path)
if filename[0] == "det_global":
np.sum(data.LAeq)
else:
np.sum(data["125Hz"])
def test_attenuation_coefficient(self):
"""Test data is all at an air pressure of one standard atmosphere, 101.325 Pa."""
data = np.loadtxt(data_path() + 'absorption_coefficient.csv', skiprows=1, delimiter=',')
f = np.array([50.0, 63.0, 80.0, 100.0, 125.0, 160.0, 200.0, 250.0, 315.0, 400.0, 500.0, 630.0, 800.0, 1000.0, 1250.0, 1600.0, 2000.0, 2500.0, 3150.0, 4000.0, 5000.0, 6300.0, 8000.0, 10000.0])
for row in data:
temperature = 273.15 + row[0] # Degrees Celsius to Kelvin
relative_humidity = row[1]
alpha = row[2:] / 1000.0 # Given in dB/km while we calculate in dB/m.
assert(f.shape==alpha.shape)
a = Atmosphere(temperature=temperature, relative_humidity=relative_humidity)
calculated_alpha = a.attenuation_coefficient(f)
np.testing.assert_array_almost_equal(alpha, calculated_alpha, decimal=2)
def test_nrc_1d():
alpha = np.array([0.1, 0.25, 0.5, 0.9])
calculated = nrc(alpha)
real = 0.4375
assert_almost_equal(calculated, real)
def test_nrc_2d():
alphas = np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.5, 0.6, 0.7]])
calculated = nrc(alphas)
real = np.array([0.25, 0.55])
assert_array_almost_equal(calculated, real)
@pytest.mark.parametrize("file_name, bands, rt, expected", [
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 't30',
np.array([7.388, 8.472, 6.795, 6.518, 4.797, 4.089])),
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 'edt',
np.array([4.667, 5.942, 6.007, 5.941, 5.038, 3.735])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't30',
np.array([0.274, 0.365, 0.303, 0.259, 0.227, 0.211, 0.204, 0.181])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't20',
np.array([0.300, 0.365, 0.151, 0.156, 0.102, 0.076, 0.146, 0.152])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't10',
np.array([0.185, 0.061, 0.109, 0.024, 0.039, 0.023, 0.105, 0.071])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 'edt',
np.array([0.267, 0.159, 0.080, 0.037, 0.021, 0.010, 0.022, 0.020])),
(data_path() + 'living_room_1.wav', third(100, 5000), 't30',
np.array([
0.318, 0.340, 0.259, 0.311, 0.267, 0.376, 0.342, 0.268, 0.212, 0.246,
0.211, 0.232, 0.192, 0.231, 0.252, 0.202, 0.184, 0.216
def test_nrc_1d():
alpha = np.array([0.1, 0.25, 0.5, 0.9])
calculated = nrc(alpha)
real = 0.4375
assert_almost_equal(calculated, real)
def test_nrc_2d():
alphas = np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.5, 0.6, 0.7]])
calculated = nrc(alphas)
real = np.array([0.25, 0.55])
assert_array_almost_equal(calculated, real)
@pytest.mark.parametrize("file_name, bands, rt, expected", [
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 't30',
np.array([7.388, 8.472, 6.795, 6.518, 4.797, 4.089])),
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 'edt',
np.array([4.667, 5.942, 6.007, 5.941, 5.038, 3.735])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't30',
np.array([0.274, 0.365, 0.303, 0.259, 0.227, 0.211, 0.204, 0.181])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't20',
np.array([0.300, 0.365, 0.151, 0.156, 0.102, 0.076, 0.146, 0.152])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't10',
np.array([0.185, 0.061, 0.109, 0.024, 0.039, 0.023, 0.105, 0.071])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 'edt',
np.array([0.267, 0.159, 0.080, 0.037, 0.021, 0.010, 0.022, 0.020])),
(data_path() + 'living_room_1.wav', third(100, 5000), 't30',
np.array([0.318, 0.340, 0.259, 0.311, 0.267, 0.376,
0.342, 0.268, 0.212, 0.246, 0.211, 0.232,
0.192, 0.231, 0.252, 0.202, 0.184, 0.216])),
def test_nrc_1d():
alpha = np.array([0.1, 0.25, 0.5, 0.9])
calculated = nrc(alpha)
real = 0.4375
assert_almost_equal(calculated, real)
def test_nrc_2d():
alphas = np.array([[0.1, 0.2, 0.3, 0.4], [0.4, 0.5, 0.6, 0.7]])
calculated = nrc(alphas)
real = np.array([0.25, 0.55])
assert_array_almost_equal(calculated, real)
@pytest.mark.parametrize("file_name, bands, rt, expected", [
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 't30',
np.array([7.388, 8.472, 6.795, 6.518, 4.797, 4.089])),
(data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 'edt',
np.array([4.667, 5.942, 6.007, 5.941, 5.038, 3.735])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't30',
np.array([0.274, 0.365, 0.303, 0.259, 0.227, 0.211, 0.204, 0.181])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't20',
np.array([0.300, 0.365, 0.151, 0.156, 0.102, 0.076, 0.146, 0.152])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 't10',
np.array([0.185, 0.061, 0.109, 0.024, 0.039, 0.023, 0.105, 0.071])),
(data_path() + 'living_room_1.wav', octave(63, 8000), 'edt',
np.array([0.267, 0.159, 0.080, 0.037, 0.021, 0.010, 0.022, 0.020])),
(data_path() + 'living_room_1.wav', third(100, 5000), 't30',
np.array([
0.318, 0.340, 0.259, 0.311, 0.267, 0.376, 0.342, 0.268, 0.212, 0.246, 0.211, 0.232, 0.192, 0.231, 0.252, 0.202,
0.184, 0.216