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Solve the following problem using Python, implementing the functions described below, one line at a time
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Description:
 def smooth(x, window_len=11, window='hanning', fill='reflect'):
 """smooth the data using a window with requested size. Convolve a normalized window with the signal. input: x: signal to be smoothed window_len: the width of the smoothing window window: the type of window from 'flat', 'hanning', 'hamming', 'bartlett', 'blackman' flat window will produce a moving average smoothing. fill: 'reflect' means that the signal is reflected onto both ends before filtering output: the smoothed signal example: t = linspace(-2, 2, 0.1) x = sin(t) + 0.1 * randn(len(t)) y = smooth(x) import seaborn pd.DataFrame({'x': x, 'y': y}, index=t).plot() SEE ALSO: numpy.hanning, numpy.hamming, numpy.bartlett, numpy.blackman numpy.convolve scipy.signal.lfilter TODO: the window parameter could be the window itself if an array instead of a string NOTE: length(output) != length(input), to correct this: instead of just y. References: http://wiki.scipy.org/Cookbook/SignalSmooth """


    # force window_len to be an odd integer so it can be symmetrically applied
    window_len = int(window_len)
    window_len += int(not (window_len % 2))
    half_len = (window_len - 1) / 2

    if x.ndim != 1:
        raise ValueError("smooth only accepts 1 dimension arrays.")
    if x.size < window_len:
        raise ValueError("Input vector needs to be bigger than window size.")
    if window_len < 3:
        return x
    if not window in ['flat', 'hanning', 'hamming', 'bartlett', 'blackman']:
        raise ValueError("The window arg ({}) should be 'flat', 'hanning', 'hamming', 'bartlett', or 'blackman'"
                         .format(window))

    s = np.r_[x[window_len - 1:0:-1], x, x[-1:-window_len:-1]]

    window = window.strip().lower()
    if window is None or window == 'flat':
        w = np.ones(window_len, 'd')
    else:
        w = getattr(np, window)(window_len)

    y = np.convolve(w / w.sum(), s, mode='valid')

    return y[half_len + 1:-half_len]