test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x1059871b0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x1059871b0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265,

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x1059871b0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x1059871b0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x1059871b0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x1059871b0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, +

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x1059871b0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x1059871b0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x1059871b0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x1059871b0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265, -

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x1059871b0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x1059871b0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x101f7f830>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x101f7f830> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265,

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x101f7f830>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x101f7f830> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x101f7f830>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x101f7f830> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, +

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x101f7f830>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x101f7f830> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x101f7f830>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x101f7f830> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265, -

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x101f7f830>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x101f7f830> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7fcdec7c8a70>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7fcdec7c8a70> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7fcdec7c8a70>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7fcdec7c8a70> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7fcdec7c8a70>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7fcdec7c8a70> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7fcdec7c8a70>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7fcdec7c8a70> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7fcdec7c8a70>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7fcdec7c8a70> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7fcdec7c8a70>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7fcdec7c8a70> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7fd88e9e5030>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7fd88e9e5030> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7fd88e9e5030>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7fd88e9e5030> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7fd88e9e5030>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7fd88e9e5030> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7fd88e9e5030>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7fd88e9e5030> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7fd88e9e5030>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7fd88e9e5030> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7fd88e9e5030>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7fd88e9e5030> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7fe7a51acbb0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7fe7a51acbb0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7fe7a51acbb0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7fe7a51acbb0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7fe7a51acbb0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7fe7a51acbb0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7fe7a51acbb0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7fe7a51acbb0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7fe7a51acbb0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7fe7a51acbb0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7fe7a51acbb0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7fe7a51acbb0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7fb55db50bf0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7fb55db50bf0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7fb55db50bf0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7fb55db50bf0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7fb55db50bf0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7fb55db50bf0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7fb55db50bf0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7fb55db50bf0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7fb55db50bf0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7fb55db50bf0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7fb55db50bf0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7fb55db50bf0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7f53dd2bea70>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7f53dd2bea70> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7f53dd2bea70>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7f53dd2bea70> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7f53dd2bea70>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7f53dd2bea70> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7f53dd2bea70>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7f53dd2bea70> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7f53dd2bea70>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7f53dd2bea70> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7f53dd2bea70>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7f53dd2bea70> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x1044827b0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x1044827b0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265,

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x1044827b0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x1044827b0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x1044827b0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x1044827b0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, +

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x1044827b0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x1044827b0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x1044827b0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x1044827b0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265, -

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x1044827b0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x1044827b0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x6000038aa300>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x6000038aa300> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x6000038aa300>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x6000038aa300> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x6000038aa300>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x6000038aa300> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x6000038aa300>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x6000038aa300> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x6000038aa300>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x6000038aa300> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x6000038aa300>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x6000038aa300> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

Process completed with exit code 1.

Process completed with exit code 1.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7fb8fc3820b0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7fb8fc3820b0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7fb8fc3820b0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7fb8fc3820b0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7fb8fc3820b0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7fb8fc3820b0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7fb8fc3820b0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7fb8fc3820b0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7fb8fc3820b0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7fb8fc3820b0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7fb8fc3820b0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7fb8fc3820b0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x7f38efd65eb0>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x7f38efd65eb0> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.277632

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x7f38efd65eb0>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x7f38efd65eb0> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x7f38efd65eb0>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x7f38efd65eb0> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265,

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x7f38efd65eb0>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x7f38efd65eb0> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x7f38efd65eb0>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x7f38efd65eb0> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265,

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x7f38efd65eb0>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x7f38efd65eb0> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x128bd10>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x128bd10> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, -

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x128bd10>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x128bd10> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x128bd10>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x128bd10> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, +

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x128bd10>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x128bd10> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x128bd10>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x128bd10> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265, -

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x128bd10>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x128bd10> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

Process completed with exit code 1.

test_call_return_vector assert np.False_ + where np.False_ = <function all at 0x2ba7b10>(array([0.0056..., 0.00563265]) == array([1.2776..., 1.27763265]) + where <function all at 0x2ba7b10> = np.all Full diff: - array([1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, - 1.27763265, 1.27763265, 1.27763265, 1.27763265, 1.27763265, -

test_run_with_return_variables AssertionError: assert ('c#' in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and 'c' not in {'a': 42.0, 'all': 0.0, 'average': 0.0, 'b$': 'abc', ...} and True and dtype('float64') == dtype('float64') and (9,) == (9,) + where True = isinstance(array([1., 1., 1., 1., 1., 1., 1., 1., 1.]), <class 'numpy.ndarray'>) + where <class 'numpy.ndarray'> = np.ndarray) + and dtype('float64') = array([1., 1., 1., 1., 1., 1., 1., 1., 1.]).dtype + and dtype('float64') = <class 'numpy.dtype'>(float) + where <class 'numpy.dtype'> = np.dtype Full diff: (9,) and np.False_ + and np.False_ = <function all at 0x2ba7b10>(array([1., 1...., 1., 1., 1.]) == [1, 1, 2, 3, 5, 8, ...] + where <function all at 0x2ba7b10> = np.all Full diff: - [1, 1, 2, 3, 5, 8, 13, 21, 34] + array([1., 1., 1., 1., 1., 1., 1., 1., 1.]))

test_xs[intensity] assert np.False_ + where np.False_ = <function all at 0x2ba7b10>(array([1.2496..., 1.24963265]) == array([0.0336..., 1.24963265]) + where <function all at 0x2ba7b10> = np.all Full diff: - array([0.03363265, 0.04163265, 0.04963265, 0.05763265, 0.06563265, - 0.07363265, 0.08163265, 0.08963265, 0.09763265, 0.10563265, - 0.11363265, 0.12163265, 0.12963265, 0.13763265, 0.14563265, - 0.15363265, 0.16163265, 0.16963265, 0.17763265, 0.18563265, - 0.19363265, 0.20163265, 0.20963265, 0.21763265, 0.22563265, - 0.23363265, 0.24163265, 0.24963265, 0.25763265, 0.26563265, - 0.27363265, 0.28163265, 0.28963265, 0.29763265, 0.30563265, - 0.31363265, 0.32163265, 0.32963265, 0.33763265, 0.34563265, - 0.35363265, 0.36163265, 0.36963265, 0.37763265, 0.38563265, - 0.39363265, 0.40163265, 0.40963265, 0.41763265, 0.42563265, - 0.43363265, 0.44163265, 0.44963265, 0.45763265, 0.46563265, - 0.47363265, 0.48163265, 0.48963265, 0.49763265, 0.50563265, - 0.51363265, 0.52163265, 0.52963265, 0.53763265, 0.54563265, - 0.55363265, 0.56163265, 0.56963265, 0.57763265, 0.58563265, - 0.59363265, 0.60163265, 0.60963265, 0.61763265, 0.62563265, - 0.63363265, 0.64163265, 0.64963265, 0.65763265, 0.66563265, - 0.67363265, 0.68163265, 0.68963265, 0.69763265, 0.70563265, - 0.71363265, 0.72163265, 0.72963265, 0.73763265, 0.74563265, - 0.75363265, 0.76163265, 0.76963265, 0.77763265, 0.78563265, - 0.79363265, 0.80163265, 0.80963265, 0.81763265, 0.82563265, - 0.83363265, 0.84163265, 0.84963265, 0.85763265, 0.86563265, - 0.87363265, 0.88163265, 0.88963265, 0.89763265, 0.90563265, - 0.91363265, 0.92163265, 0.92963265, 0.93763265, 0.94563265, - 0.95363265, 0.96163265, 0.96963265, 0.97763265, 0.98563265, - 0.99363265, 1.00163265, 1.00963265, 1.01763265, 1.02563265, - 1.03363265, 1.04163265, 1.04963265, 1.05763265, 1.06563265, - 1.07363265, 1.08163265, 1.08963265, 1.09763265, 1.10563265, - 1.11363265, 1.12163265, 1.12963265, 1.13763265, 1.14563265, - 1.15363265, 1.16163265, 1.16963265, 1.17763265, 1.18563265, - 1.19363265, 1.20163265, 1.20963265, 1.21763265, 1.22563265, + array([1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, + 1.24963265, 1.24963265, 1.24963265, 1.24963265, 1.24963265, +

test_xs[pitch] assert np.False_ + where np.False_ = <function all at 0x2ba7b10>(array([1.2616..., 1.26163265]) == array([0.0216..., 1.26163265]) + where <function all at 0x2ba7b10> = np.all Full diff: + array([1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - array([0.02163265, 0.03163265, 0.04163265, 0.05163265, 0.06163265, - 0.07163265, 0.08163265, 0.09163265, 0.10163265, 0.11163265, - 0.12163265, 0.13163265, 0.14163265, 0.15163265, 0.16163265, - 0.17163265, 0.18163265, 0.19163265, 0.20163265, 0.21163265, - 0.22163265, 0.23163265, 0.24163265, 0.25163265, 0.26163265, - 0.27163265, 0.28163265, 0.29163265, 0.30163265, 0.31163265, - 0.32163265, 0.33163265, 0.34163265, 0.35163265, 0.36163265, - 0.37163265, 0.38163265, 0.39163265, 0.40163265, 0.41163265, - 0.42163265, 0.43163265, 0.44163265, 0.45163265, 0.46163265, - 0.47163265, 0.48163265, 0.49163265, 0.50163265, 0.51163265, - 0.52163265, 0.53163265, 0.54163265, 0.55163265, 0.56163265, - 0.57163265, 0.58163265, 0.59163265, 0.60163265, 0.61163265, - 0.62163265, 0.63163265, 0.64163265, 0.65163265, 0.66163265, - 0.67163265, 0.68163265, 0.69163265, 0.70163265, 0.71163265, - 0.72163265, 0.73163265, 0.74163265, 0.75163265, 0.76163265, - 0.77163265, 0.78163265, 0.79163265, 0.80163265, 0.81163265, - 0.82163265, 0.83163265, 0.84163265, 0.85163265, 0.86163265, - 0.87163265, 0.88163265, 0.89163265, 0.90163265, 0.91163265, - 0.92163265, 0.93163265, 0.94163265, 0.95163265, 0.96163265, - 0.97163265, 0.98163265, 0.99163265, 1.00163265, 1.01163265, - 1.02163265, 1.03163265, 1.04163265, 1.05163265, 1.06163265, - 1.07163265, 1.08163265, 1.09163265, 1.10163265, 1.11163265, - 1.12163265, 1.13163265, 1.14163265, 1.15163265, 1.16163265, - 1.17163265, 1.18163265, 1.19163265, 1.20163265, 1.21163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, ? ^^ ^^ ^^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265, - 1.22163265, 1.23163265, 1.24163265, 1.25163265, 1.26163265], ? ^ ^ ^ ^ + 1.26163265, 1.26163265, 1.26163265, 1.26163265, 1.26163265], ? ^ ^ ^

test_xs[spectrogram] assert np.False_ + where np.False_ = <function all at 0x2ba7b10>(array([1.2776..., 1.27763265]) == array([0.0056..., 1.27763265]) + where <function all at 0x2ba7b10> = np.all Full diff: - array([0.00563265, 0.00763265, 0.00963265, 0.01163265, 0.01363265, - 0.01563265, 0.01763265, 0.01963265, 0.02163265, 0.02363265, - 0.02563265, 0.02763265, 0.02963265, 0.03163265, 0.03363265, - 0.03563265, 0.03763265, 0.03963265, 0.04163265, 0.04363265, - 0.04563265, 0.04763265, 0.04963265, 0.05163265, 0.05363265, - 0.05563265, 0.05763265, 0.05963265, 0.06163265, 0.06363265, - 0.06563265, 0.06763265, 0.06963265, 0.07163265, 0.07363265, - 0.07563265, 0.07763265, 0.07963265, 0.08163265, 0.08363265, - 0.08563265, 0.08763265, 0.08963265, 0.09163265, 0.09363265, - 0.09563265, 0.09763265, 0.09963265, 0.10163265, 0.10363265, - 0.10563265, 0.10763265, 0.10963265, 0.11163265, 0.11363265, - 0.11563265, 0.11763265, 0.11963265, 0.12163265, 0.12363265, - 0.12563265, 0.12763265, 0.12963265, 0.13163265, 0.13363265, - 0.13563265, 0.13763265, 0.13963265, 0.14163265, 0.14363265, - 0.14563265, 0.14763265, 0.14963265, 0.15163265, 0.15363265, - 0.15563265, 0.15763265, 0.15963265, 0.16163265, 0.16363265, - 0.16563265, 0.16763265, 0.16963265, 0.17163265, 0.17363265, - 0.17563265, 0.17763265, 0.17963265, 0.18163265, 0.18363265, - 0.18563265, 0.18763265, 0.18963265, 0.19163265, 0.19363265, - 0.19563265, 0.19763265, 0.19963265, 0.20163265, 0.20363265, - 0.20563265, 0.20763265, 0.20963265, 0.21163265, 0.21363265, - 0.21563265, 0.21763265, 0.21963265, 0.22163265, 0.22363265, - 0.22563265, 0.22763265, 0.22963265, 0.23163265, 0.23363265, - 0.23563265, 0.23763265, 0.23963265, 0.24163265, 0.24363265, - 0.24563265, 0.24763265, 0.24963265, 0.25163265, 0.25363265, - 0.25563265, 0.25763265, 0.25963265, 0.26163265, 0.26363265, - 0.26563265, 0.26763265, 0.26963265, 0.27163265, 0.27363265, - 0.27563265, 0.27763265, 0.27963265, 0.28163265, 0.28363265, - 0.28563265, 0.28763265, 0.28963265, 0.29163265, 0.29363265, - 0.29563265, 0.29763265, 0.29963265, 0.30163265, 0.30363265, - 0.30563265, 0.30763265, 0.30963265, 0.31163265, 0.31363265, - 0.31563265, 0.31763265, 0.31963265, 0.32163265, 0.32363265, - 0.32563265, 0.32763265, 0.32963265, 0.33163265, 0.33363265, - 0.33563265, 0.33763265, 0.33963265, 0.34163265, 0.34363265, - 0.34563265, 0.34763265, 0.34963265, 0.35163265, 0.35363265, - 0.35563265, 0.35763265, 0.35963265, 0.36163265, 0.36363265, - 0.36563265, 0.36763265, 0.36963265, 0.37163265, 0.37363265, - 0.37563265, 0.37763265, 0.37963265, 0.38163265, 0.38363265, - 0.38563265, 0.38763265, 0.38963265, 0.39163265, 0.39363265, - 0.39563265, 0.39763265, 0.39963265, 0.40163265, 0.40363265, - 0.40563265, 0.40763265, 0.40963265, 0.41163265, 0.41363265, - 0.41563265, 0.41763265, 0.41963265, 0.42163265, 0.42363265, - 0.42563265, 0.42763265, 0.42963265, 0.43163265, 0.43363265, - 0.43563265, 0.43763265, 0.43963265, 0.44163265, 0.44363265, - 0.44563265, 0.44763265, 0.44963265, 0.45163265, 0.45363265, - 0.45563265, 0.45763265, 0.45963265, 0.46163265, 0.46363265, - 0.46563265, 0.46763265, 0.46963265, 0.47163265, 0.47363265, - 0.47563265, 0.47763265, 0.47963265, 0.48163265, 0.48363265, - 0.48563265, 0.48763265, 0.48963265, 0.49163265, 0.49363265, - 0.49563265, 0.49763265, 0.49963265, 0.50163265, 0.50363265, - 0.50563265, 0.50763265, 0.50963265, 0.51163265, 0.51363265, - 0.51563265, 0.51763265, 0.51963265, 0.52163265, 0.52363265, - 0.52563265, 0.52763265, 0.52963265, 0.53163265, 0.53363265, -

test_xs[sound] assert np.False_ + where np.False_ = <function all at 0x2ba7b10>(array([1.2832... 1.28325397]) == array([1.1337...28325397e+00]) + where <function all at 0x2ba7b10> = np.all Full diff: - array([1.13378685e-05, 3.40136054e-05, 5.66893424e-05, ..., - 1.28320862e+00, 1.28323129e+00, 1.28325397e+00], + array([1.28325397, 1.28325397, 1.28325397, ..., 1.28325397, 1.28325397, + 1.28325397], ))

Process completed with exit code 2.

Process completed with exit code 1.

Process completed with exit code 1.

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 99 bytes into a region of size between 54 and 452 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 15 and 244 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 15 and 244 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 10 bytes into a region of size 9 [-Wformat-overflow=]

‘%c’ directive writing 1 byte into a region of size between 0 and 9 [-Wformat-overflow=]

ignoring return value of ‘size_t fread(void*, size_t, size_t, FILE*)’ declared with attribute ‘warn_unused_result’ [-Wunused-result]

‘fastsdct’ accessing 72 bytes in a region of size 68 [-Wstringop-overflow=]

‘__builtin___sprintf_chk’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 15 and 244 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 15 and 244 [-Wformat-overflow=]

ignoring return value of ‘char* strerror_r(int, char*, size_t)’ declared with attribute ‘warn_unused_result’ [-Wunused-result]

iteration 5 invokes undefined behavior [-Waggressive-loop-optimizations]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

‘%d’ directive writing between 1 and 11 bytes into a region of size between 0 and 9 [-Wformat-overflow=]

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

taking the absolute value of unsigned type 'unsigned int' has no effect [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

using floating point absolute value function 'fabs' when argument is of integer type [-Wabsolute-value]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 49 bytes into a region of size between 19 and 100 [-Wformat-overflow=]

‘sprintf’ may write a terminating nul past the end of the destination [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 58 [-Wformat-overflow=]

‘%s’ directive writing up to 59 bytes into a region of size 52 [-Wformat-overflow=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive output may be truncated writing up to 199 bytes into a region of size between 1 and 200 [-Wformat-truncation=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

‘%s’ directive writing up to 39 bytes into a region of size between 19 and 260 [-Wformat-overflow=]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

] [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

'stdext::checked_array_iterator<T *>': warning STL4043: stdext::checked_array_iterator, stdext::unchecked_array_iterator, and related factory functions are non-Standard extensions and will be removed in the future. std::span (since C++20) and gsl::span can be used instead. You can define _SILENCE_STDEXT_ARR_ITERS_DEPRECATION_WARNING or _SILENCE_ALL_MS_EXT_DEPRECATION_WARNINGS to suppress this warning. [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

with [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

[ [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]

T=char [D:\a\Parselmouth\Parselmouth\build\praat\praat.vcxproj]