librosa/core/time_frequency.py
def times_like(X, sr=22050, hop_length=512, n_fft=None, axis=-1):
    """Return an array of time values to match the time axis from a feature matrix.

    Parameters
    ----------
    X : np.ndarray or scalar
        - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram.
        - If scalar, X represents the number of frames.

    sr : number > 0 [scalar]
        audio sampling rate

    hop_length : int > 0 [scalar]
        number of samples between successive frames

    n_fft : None or int > 0 [scalar]
        Optional: length of the FFT window.
        If given, time conversion will include an offset of `n_fft / 2`
        to counteract windowing effects when using a non-centered STFT.

    axis : int [scalar]
        The axis representing the time axis of X.
        By default, the last axis (-1) is taken.

    Returns
    -------
    times : np.ndarray [shape=(n,)]
        ndarray of times (in seconds) corresponding to each frame of X.

    See Also
    --------
    samples_like : Return an array of sample indices to match the time axis from a feature matrix.

    Examples
    --------
    Provide a feature matrix input:

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> X = librosa.stft(y)
    >>> times = librosa.times_like(X)
    >>> times
    array([  0.00000000e+00,   2.32199546e-02,   4.64399093e-02, ...,
             6.13935601e+01,   6.14167800e+01,   6.14400000e+01])

    Provide a scalar input:

    >>> n_frames = 2647
    >>> times = librosa.times_like(n_frames)
    >>> times
    array([  0.00000000e+00,   2.32199546e-02,   4.64399093e-02, ...,
             6.13935601e+01,   6.14167800e+01,   6.14400000e+01])
    """
    samples = samples_like(X, hop_length=hop_length, n_fft=n_fft, axis=axis)
    return samples_to_time(samples, sr=sr)
def times_like(X, sr=22050, hop_length=512, n_fft=None, axis=-1):
    samples = samples_like(X, hop_length=hop_length, n_fft=n_fft, axis=axis)
    return samples_to_time(samples, sr=sr)
def times_like ( X , sr = 22050 , hop_length = 512 , n_fft = None , axis = - 1 ) : samples = samples_like ( X , hop_length = hop_length , n_fft = n_fft , axis = axis ) return samples_to_time ( samples , sr = sr )
Return an array of time values to match the time axis from a feature matrix.

    Parameters
    ----------
    X : np.ndarray or scalar
        - If ndarray, X is a feature matrix, e.g. STFT, chromagram, or mel spectrogram.
        - If scalar, X represents the number of frames.

    sr : number > 0 [scalar]
        audio sampling rate

    hop_length : int > 0 [scalar]
        number of samples between successive frames

    n_fft : None or int > 0 [scalar]
        Optional: length of the FFT window.
        If given, time conversion will include an offset of `n_fft / 2`
        to counteract windowing effects when using a non-centered STFT.

    axis : int [scalar]
        The axis representing the time axis of X.
        By default, the last axis (-1) is taken.

    Returns
    -------
    times : np.ndarray [shape=(n,)]
        ndarray of times (in seconds) corresponding to each frame of X.

    See Also
    --------
    samples_like : Return an array of sample indices to match the time axis from a feature matrix.

    Examples
    --------
    Provide a feature matrix input:

    >>> y, sr = librosa.load(librosa.util.example_audio_file())
    >>> X = librosa.stft(y)
    >>> times = librosa.times_like(X)
    >>> times
    array([  0.00000000e+00,   2.32199546e-02,   4.64399093e-02, ...,
             6.13935601e+01,   6.14167800e+01,   6.14400000e+01])

    Provide a scalar input:

    >>> n_frames = 2647
    >>> times = librosa.times_like(n_frames)
    >>> times
    array([  0.00000000e+00,   2.32199546e-02,   4.64399093e-02, ...,
             6.13935601e+01,   6.14167800e+01,   6.14400000e+01])
Return an array of time values to match the time axis from a feature matrix .