kopia lustrzana https://github.com/corrscope/corrscope
250 wiersze
7.7 KiB
Python
250 wiersze
7.7 KiB
Python
import warnings
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from typing import Sequence
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import numpy as np
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from numpy.testing import assert_allclose
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import pytest
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from delayed_assert import expect, assert_expectations
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from corrscope.config import CorrError
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from corrscope.utils.scipy.wavfile import WavFileWarning
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from corrscope.wave import Wave, Flatten, calc_flatten_matrix
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prefix = "tests/wav-formats/"
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wave_paths = [
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# 2000 samples, with a full-scale peak at data[1000].
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"u8-impulse1000.wav",
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"s16-impulse1000.wav",
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"s32-impulse1000.wav",
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"f32-impulse1000.wav",
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"f64-impulse1000.wav",
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]
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@pytest.mark.parametrize("wave_path", wave_paths)
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def test_wave(wave_path):
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with warnings.catch_warnings(record=True) as w:
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# Cause all warnings to always be triggered.
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warnings.simplefilter("always")
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wave = Wave(prefix + wave_path)
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data = wave[:]
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# Audacity dithers <=16-bit WAV files upon export, creating a few bits of noise.
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# As a result, amin(data) <= 0.
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assert -0.01 < np.amin(data) <= 0
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assert 0.99 < np.amax(data) <= 1
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# check for FutureWarning (raised when determining wavfile type)
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warns = [o for o in w if issubclass(o.category, FutureWarning)]
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assert not [str(w) for w in warns]
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# Stereo tests
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def arr(*args):
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return np.array(args)
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def test_calc_flatten_matrix():
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nchan = 3
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# Test Stereo
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np.testing.assert_equal(calc_flatten_matrix(Flatten.Stereo, nchan), np.eye(nchan))
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# Test SumAvg on various channel counts
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 1), [1])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 2), [0.5, 0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.SumAvg, 4), [0.25] * 4)
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# Test DiffAvg on various channel counts
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# (Wave will use Mono instead of DiffAvg, on mono audio signals.
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# But ensure it doesn't crash anyway.)
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 1), [0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 2), [0.5, -0.5])
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np.testing.assert_equal(calc_flatten_matrix(Flatten.DiffAvg, 4), [0.5, -0.5, 0, 0])
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# Test Mono
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np.testing.assert_equal(calc_flatten_matrix(Flatten.Mono, 1), [1])
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# Test custom strings and delimiters
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out = arr(1, 2, 1)
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nchan = 3
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np.testing.assert_equal(calc_flatten_matrix(",1,2,1,", nchan), out / sum(out))
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np.testing.assert_equal(calc_flatten_matrix(" 1, 2, 1 ", nchan), out / sum(out))
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np.testing.assert_equal(calc_flatten_matrix("1 2 1", nchan), out / sum(out))
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# Test negative values
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nchan = 2
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np.testing.assert_equal(calc_flatten_matrix("1, -1", nchan), arr(1, -1) / 2)
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np.testing.assert_equal(calc_flatten_matrix("-1, 1", nchan), arr(-1, 1) / 2)
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np.testing.assert_equal(calc_flatten_matrix("-1, -1", nchan), arr(-1, -1) / 2)
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# Test invalid inputs
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with pytest.raises(CorrError):
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calc_flatten_matrix("", 0)
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with pytest.raises(CorrError):
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calc_flatten_matrix("1 -1 uwu", 3)
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with pytest.raises(CorrError):
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calc_flatten_matrix("0 0", 2)
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def test_stereo_merge():
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"""Test indexing Wave by slices *or* ints. Flatten using default SumAvg mode."""
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# Contains a full-scale sine wave in left channel, and silence in right.
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# λ=100, nsamp=2000
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wave = Wave(prefix + "stereo-sine-left-2000.wav")
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period = 100
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nsamp = 2000
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# [-1, 1) from [-32768..32768)
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int16_step = (1 - -1) / (2 ** 16)
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assert int16_step == 2 ** -15
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# Check wave indexing dimensions.
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assert wave[0].shape == ()
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assert wave[:].shape == (nsamp,)
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# Check stereo merging.
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assert_allclose(wave[0], 0)
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assert_allclose(wave[period], 0)
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assert_allclose(wave[period // 4], 0.5, atol=int16_step)
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def check_bound(obj):
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amax = np.amax(obj)
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assert amax.shape == ()
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assert_allclose(amax, 0.5, atol=int16_step)
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assert_allclose(np.amin(obj), -0.5, atol=int16_step)
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check_bound(wave[:])
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AllFlattens = [*Flatten.__members__.values(), "1 1", "1 0", "1 -1"]
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@pytest.mark.parametrize("flatten", AllFlattens)
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@pytest.mark.parametrize("return_channels", [False, True])
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@pytest.mark.parametrize(
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"path,nchan,peaks",
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[
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("tests/sine440.wav", 1, [0.5]),
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("tests/stereo in-phase.wav", 2, [1, 1]),
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("tests/wav-formats/stereo-sine-left-2000.wav", 2, [1, 0]),
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],
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)
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def test_stereo_flatten_modes(
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flatten: Flatten,
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return_channels: bool,
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path: str,
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nchan: int,
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peaks: Sequence[float],
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):
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"""Ensures all Flatten modes are handled properly
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for stereo and mono signals."""
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# return_channels=False <-> triggering.
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# flatten=stereo -> rendering.
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# These conditions do not currently coexist.
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# if not return_channels and flatten == Flatten.Stereo:
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# return
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assert nchan == len(peaks)
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wave = Wave(path)
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if flatten is Flatten.Mono:
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with pytest.raises(CorrError):
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wave.with_flatten(flatten, return_channels)
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return
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else:
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wave = wave.with_flatten(flatten, return_channels)
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nsamp = wave.nsamp
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data = wave[:]
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# wave.data == 2-D array of shape (nsamp, nchan)
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if flatten == Flatten.Stereo:
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assert data.shape == (nsamp, nchan)
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for chan_data, peak in zip(data.T, peaks):
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assert_full_scale(chan_data, peak)
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else:
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if return_channels:
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assert data.shape == (nsamp, 1)
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else:
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assert data.shape == (nsamp,)
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# If DiffAvg and in-phase, L-R=0.
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if flatten == Flatten.DiffAvg:
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if len(peaks) >= 2 and peaks[0] == peaks[1]:
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np.testing.assert_equal(data, 0)
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else:
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pass
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# If SumAvg, check average.
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elif flatten == Flatten.SumAvg:
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assert_full_scale(data, np.mean(peaks))
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# Don't test custom string modes for now.
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def assert_full_scale(data, peak):
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peak = abs(peak)
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assert np.amax(data) == pytest.approx(peak, rel=0.01)
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assert np.amin(data) == pytest.approx(-peak, rel=0.01)
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def test_stereo_mmap():
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wave = Wave(prefix + "stereo-sine-left-2000.wav")
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assert isinstance(wave.data, np.memmap)
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# Miscellaneous tests
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def test_wave_subsampling():
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wave = Wave("tests/sine440.wav")
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# period = 48000 / 440 = 109.(09)*
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wave.get_around(1000, return_nsamp=501, stride=4)
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# len([:region_len:subsampling]) == ceil(region_len / subsampling)
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# If region_len % subsampling != 0, len() != region_len // subsampling.
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stride = 4
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region = 100 # diameter = region * stride
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for i in [-1000, 50000]:
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data = wave.get_around(i, region, stride)
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assert (data == 0).all()
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def test_stereo_doesnt_overflow():
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""" Ensure loud stereo tracks do not overflow. """
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wave = Wave("tests/stereo in-phase.wav")
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samp = 100
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stride = 1
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data = wave.get_around(wave.nsamp // 2, samp, stride)
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expect(np.amax(data) > 0.99)
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expect(np.amin(data) < -0.99)
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# In the absence of overflow, sine waves have no large jumps.
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# In the presence of overflow, stereo sum will jump between INT_MAX and INT_MIN.
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# np.mean and rescaling converts to 0.499... and -0.5, which is nearly 1.
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expect(np.amax(np.abs(np.diff(data))) < 0.5)
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assert_expectations()
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def test_header_larger_than_filesize():
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"""According to Zeinok, VortexTracker 2.5 produces slightly corrupted WAV files
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whose RIFF header metadata indicates a filesize larger than the actual filesize.
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Most programs read the audio chunk fine.
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Scipy normally rejects such files, raises ValueError("Unexpected end of file.")
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My version instead accepts such files (but warns WavFileWarning).
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"""
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with pytest.warns(WavFileWarning):
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wave = Wave("tests/header larger than filesize.wav")
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assert wave
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