Module « scipy.signal »

Fonction resample - module scipy.signal

Signature de la fonction resample

def resample(x, num, t=None, axis=0, window=None, domain='time') 

Description

resample.__doc__

    Resample `x` to `num` samples using Fourier method along the given axis.

    The resampled signal starts at the same value as `x` but is sampled
    with a spacing of ``len(x) / num * (spacing of x)``.  Because a
    Fourier method is used, the signal is assumed to be periodic.

    Parameters
    ----------
    x : array_like
        The data to be resampled.
    num : int
        The number of samples in the resampled signal.
    t : array_like, optional
        If `t` is given, it is assumed to be the equally spaced sample
        positions associated with the signal data in `x`.
    axis : int, optional
        The axis of `x` that is resampled.  Default is 0.
    window : array_like, callable, string, float, or tuple, optional
        Specifies the window applied to the signal in the Fourier
        domain.  See below for details.
    domain : string, optional
        A string indicating the domain of the input `x`:
        ``time`` Consider the input `x` as time-domain (Default),
        ``freq`` Consider the input `x` as frequency-domain.

    Returns
    -------
    resampled_x or (resampled_x, resampled_t)
        Either the resampled array, or, if `t` was given, a tuple
        containing the resampled array and the corresponding resampled
        positions.

    See Also
    --------
    decimate : Downsample the signal after applying an FIR or IIR filter.
    resample_poly : Resample using polyphase filtering and an FIR filter.

    Notes
    -----
    The argument `window` controls a Fourier-domain window that tapers
    the Fourier spectrum before zero-padding to alleviate ringing in
    the resampled values for sampled signals you didn't intend to be
    interpreted as band-limited.

    If `window` is a function, then it is called with a vector of inputs
    indicating the frequency bins (i.e. fftfreq(x.shape[axis]) ).

    If `window` is an array of the same length as `x.shape[axis]` it is
    assumed to be the window to be applied directly in the Fourier
    domain (with dc and low-frequency first).

    For any other type of `window`, the function `scipy.signal.get_window`
    is called to generate the window.

    The first sample of the returned vector is the same as the first
    sample of the input vector.  The spacing between samples is changed
    from ``dx`` to ``dx * len(x) / num``.

    If `t` is not None, then it is used solely to calculate the resampled
    positions `resampled_t`

    As noted, `resample` uses FFT transformations, which can be very
    slow if the number of input or output samples is large and prime;
    see `scipy.fft.fft`.

    Examples
    --------
    Note that the end of the resampled data rises to meet the first
    sample of the next cycle:

    >>> from scipy import signal

    >>> x = np.linspace(0, 10, 20, endpoint=False)
    >>> y = np.cos(-x**2/6.0)
    >>> f = signal.resample(y, 100)
    >>> xnew = np.linspace(0, 10, 100, endpoint=False)

    >>> import matplotlib.pyplot as plt
    >>> plt.plot(x, y, 'go-', xnew, f, '.-', 10, y[0], 'ro')
    >>> plt.legend(['data', 'resampled'], loc='best')
    >>> plt.show()