def setup_module(imaging): imaging.octaves = octave(16, 16000) imaging.thirds = third(63, 8000) imaging.tl_oct = np.array([3, 4, 5, 12, 15, 24, 28, 23, 35, 45, 55]) imaging.tl_third = np.array([0, 0, 0, 1, 1, 2, 3, 5, 8, 13, 21, 32, 41, 47, 46, 44, 58, 77, 61, 75, 56, 54]) imaging.title = 'Title' imaging.label = 'Label'
def setup_module(imaging): imaging.octaves = octave(16, 16000) imaging.thirds = third(63, 8000) imaging.tl_oct = np.array([3, 4, 5, 12, 15, 24, 28, 23, 35, 45, 55]) imaging.tl_third = np.array([ 0, 0, 0, 1, 1, 2, 3, 5, 8, 13, 21, 32, 41, 47, 46, 44, 58, 77, 61, 75, 56, 54 ]) imaging.title = 'Title' imaging.label = 'Label'
def rt60(wav, img): plt.cla() # Clear axis plt.clf() # Clear figure plt.close() # Close a figure window filePath = wav octave_bands = band.octave(100, 8000) sample_rate, data = wavfile.read(filePath) plt.ylabel('Freqency (hz)') plt.xlabel('Time(sec)') spectrum, freqs, t, im = plt.specgram( data, Fs=sample_rate, NFFT=1024, cmap=plt.get_cmap('twilight_shifted_r')) t60 = room.t60_impulse(filePath, octave_bands, 't30') plt.savefig(img) np.set_printoptions( formatter={'float_kind': lambda x: "{0:0.3f}".format(x)}) return np.round(t60, 3)
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([7.24027654, 8.47019681, 6.79466752, 6.51780663, 4.79692643, 4.08912686])), (data_path() + 'ir_sportscentre_omni.wav', octave(125, 4000), 'edt', np.array([4.71644743, 5.94075422, 6.00702329, 5.94062563, 5.03778274, 3.73465316])), (data_path() + 'living_room_1.wav', octave(63, 8000), 't30', np.array([0.27658574, 0.36466480, 0.30282462, 0.25946725, 0.22710926, 0.21056449, 0.20445301, 0.18080435])), (data_path() + 'living_room_1.wav', octave(63, 8000), 't20', np.array([0.30418539, 0.36486166, 0.15138373, 0.15594470, 0.10192937, 0.07587109, 0.14564938, 0.15231023])), (data_path() + 'living_room_1.wav', octave(63, 8000), 't10', np.array([0.18067203, 0.06121885, 0.10898306, 0.02377203, 0.03865264, 0.02303814, 0.10484486, 0.07141563])), (data_path() + 'living_room_1.wav', octave(63, 8000), 'edt',
def test_octave(octave_real): generated = octave(16, 16000) real = octave_real assert_array_equal(generated, real)
def test_octave(): generated = octave(16, 16000) real = octave_real assert_array_equal(generated, real)
def set_default_locators_and_formatters(self, axis): axis.set_major_locator(FixedLocator(octave(16, 16000))) axis.set_major_formatter(ScalarFormatter()) axis.set_minor_locator(NullLocator()) axis.set_minor_formatter(NullFormatter())
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])),