o ٷiU#@s\dZddlZddlZddlZddlmZddlmZm Z ddgZ d d dZ d d dZ dS)zCore resampling interfaceN) get_filter) resample_f_s resample_f_presample resample_nu kaiser_bestFcKs|dkr td||dkrtd|||kr|St||}t|j}t||t|t|||<||dkrKtd|j|||t|j tj rWtj } n|j } tj || |d} t |fi|\} } } |dkru|| } tj| | dd}td |}d |}t|||}|rzt|d||| || || d|W| Stjy}z#tj|d d d t|d||| || || d|WYd }~| Sd }~wwt|d||| || || d|| S)a Resample a signal x from sr_orig to sr_new along a given axis. Parameters ---------- x : np.ndarray, dtype=np.float* The input signal(s) to resample. sr_orig : int > 0 The sampling rate of x sr_new : int > 0 The target sampling rate of the output signal(s) If `sr_new == sr_orig`, then a copy of `x` is returned with no interpolation performed. axis : int The target axis along which to resample `x` filter : optional, str or callable The resampling filter to use. By default, uses the `kaiser_best` (pre-computed filter). parallel : optional, bool Enable/disable parallel computation exploiting multi-threading. Default: False. **kwargs additional keyword arguments provided to the specified filter Returns ------- y : np.ndarray `x` resampled to `sr_new` Raises ------ ValueError if `sr_orig` or `sr_new` is not positive TypeError if the input signal `x` has an unsupported data type. Examples -------- >>> import resampy >>> np.set_printoptions(precision=3, suppress=True) >>> # Generate a sine wave at 440 Hz for 5 seconds >>> sr_orig = 44100.0 >>> x = np.sin(2 * np.pi * 440.0 / sr_orig * np.arange(5 * sr_orig)) >>> x array([ 0. , 0.063, ..., -0.125, -0.063]) >>> # Resample to 22050 with default parameters >>> resampy.resample(x, sr_orig, 22050) array([ 0.011, 0.122, 0.25 , ..., -0.366, -0.25 , -0.122]) >>> # Resample using the fast (low-quality) filter >>> resampy.resample(x, sr_orig, 22050, filter='kaiser_fast') array([ 0.012, 0.121, 0.251, ..., -0.365, -0.251, -0.121]) >>> # Resample using a high-quality filter >>> resampy.resample(x, sr_orig, 22050, filter='kaiser_best') array([ 0.011, 0.122, 0.25 , ..., -0.366, -0.25 , -0.122]) >>> # Resample using a Hann-windowed sinc filter >>> import scipy.signal >>> resampy.resample(x, sr_orig, 22050, filter='sinc_window', ... window=scipy.signal.hann) array([ 0.011, 0.123, 0.25 , ..., -0.366, -0.25 , -0.123]) >>> # Generate stereo data >>> x_right = np.sin(2 * np.pi * 880.0 / sr_orig * np.arange(len(x))) >>> x_stereo = np.stack([x, x_right]) >>> x_stereo.shape (2, 220500) >>> # Resample along the time axis (1) >>> y_stereo = resampy.resample(x_stereo, sr_orig, 22050, axis=1) >>> y_stereo.shape (2, 110250) rInvalid sample rate: sr_orig={}zInvalid sample rate: sr_new={}rz;Input signal length={} is too small to resample from {}->{}dtypeshaperappend?$ Fallback to the sequential version. stacklevelN) ValueErrorformatcopyfloatlistr intnp issubdtyper integerfloat32 zeros_likerdiffminarangerswapaxesnumba TypingErrorwarningswarnr)xsr_origsr_newaxisfilterparallelkwargs sample_ratior r y interp_win precision_ interp_deltascaletime_incrementt_outexcr9@/home/ubuntu/.local/lib/python3.10/site-packages/resampy/core.pyrsR       "     cKs|dkr td|t|}|jdkrtd|jt|dks2t||j|d|krHtdt|t||j|d|t|j}t |||<t |j tj r_tj }n|j }tj|||d} t|fi|\} } } tj| | dd} |d kr||}|rzt|d||| | | d | d|W| Stjy}z#tj|d d d t|d||| | | d | d|WYd }~| Sd }~wwt|d||| | | d | d|| S)a'Interpolate a signal x at specified positions (t_out) along a given axis. Parameters ---------- x : np.ndarray, dtype=np.float* The input signal(s) to resample. sr_orig : float Sampling rate of the input signal (x). t_out : np.ndarray, dtype=np.float* Position of the output samples. axis : int The target axis along which to resample `x` filter : optional, str or callable The resampling filter to use. By default, uses the `kaiser_best` (pre-computed filter). parallel : optional, bool Enable/disable parallel computation exploiting multi-threading. Default: True. **kwargs additional keyword arguments provided to the specified filter Returns ------- y : np.ndarray `x` resampled to `t_out` Raises ------ TypeError if the input signal `x` has an unsupported data type. Notes ----- Differently form the `resample` function the filter `rolloff` is not automatically adapted in case of subsampling. For this reason results obtained with the `resample_nu` could be slightly different form the ones obtained with `resample` if the filter parameters are not carefully set by the user. Examples -------- >>> import resampy >>> np.set_printoptions(precision=3, suppress=True) >>> # Generate a sine wave at 100 Hz for 5 seconds >>> sr_orig = 100.0 >>> f0 = 1 >>> t = np.arange(5 * sr_orig) / sr_orig >>> x = np.sin(2 * np.pi * f0 * t) >>> x array([ 0. , 0.063, 0.125, ..., -0.187, -0.125, -0.063]) >>> # Resample to non-uniform sampling >>> t_new = np.log2(1 + t)[::5] - t[0] >>> resampy.resample_nu(x, sr_orig, t_new) array([ 0.001, 0.427, 0.76 , ..., -0.3 , -0.372, -0.442]) rr rz+Invalid t_out shape ({}), 1D array expectedz6Output domain [{}, {}] exceeds the data domain [0, {}]r rrrrrrN)rrrasarrayndimr r!maxrlenrr rrrrr rr#r$r%r&r'r)r(r)r7r+r,r-r.r r r0r1r2r3r4r8r9r9r:rs|B   *     "     )rr F) __doc__r&numpyrr$filtersrinterpnrr__all__rrr9r9r9r:s  '