corrscope/tests/test_wave.py

import warnings

import numpy as np

import pytest

from ovgenpy.wave import Wave


prefix = 'tests/wav-formats/'
wave_paths = [
    # 2000 samples, with a full-scale peak at data[1000].
    'u8-impulse1000.wav',
    's16-impulse1000.wav',
    's32-impulse1000.wav',
    'f32-impulse1000.wav',
    'f64-impulse1000.wav',
]


@pytest.mark.parametrize("wave_path", wave_paths)
def test_wave(wave_path):
    with warnings.catch_warnings(record=True) as w:
        # Cause all warnings to always be triggered.
        warnings.simplefilter("always")

        wave = Wave(None, prefix + wave_path)
        data = wave[:]

        # Audacity dithers <=16-bit WAV files upon export, creating a few bits of noise.
        # As a result, amin(data) <= 0.
        assert -0.01 < np.amin(data) <= 0
        assert 0.99 < np.amax(data) <= 1

        # check for FutureWarning (raised when determining wavfile type)
        warns = [o for o in w if issubclass(o.category, FutureWarning)]
        assert not [str(w) for w in warns]


def test_wave_subsampling():
    wave = Wave(None, 'tests/sine440.wav')
    # period = 48000 / 440 = 109.(09)*

    wave.get_around(1000, region_nsamp=501, stride=4)
    # len([:region_len:subsampling]) == ceil(region_len / subsampling)
    # If region_len % subsampling != 0, len() != region_len // subsampling.

    stride = 4
    region = 100    # diameter = region * stride
    for i in [-1000, 50000]:
        data = wave.get_around(i, region, stride)
        assert (data == 0).all()


def test_stereo_doesnt_overflow():
    """ Ensure loud stereo tracks do not overflow. """
    wave = Wave(None, 'tests/stereo in-phase.wav')

    samp = 100
    stride = 1
    data = wave.get_around(wave.nsamp // 2, samp, stride)
    assert np.amax(data) > 0.99
    assert np.amin(data) < -0.99

    # In the absence of overflow, sine waves have no large jumps.
    # In the presence of overflow, stereo sum will jump between INT_MAX and INT_MIN.
    # np.mean and rescaling converts to 0.499... and -0.5, which is nearly 1.
    assert np.amax(np.abs(np.diff(data))) < 0.5