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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 svd(stream_list, full=False):
 """ Compute the SVD of a number of templates. Returns the singular vectors and singular values of the templates. :type stream_list: List of :class: obspy.Stream :param stream_list: List of the templates to be analysed :type full: bool :param full: Whether to compute the full input vector matrix or not. :return: SValues(list) for each channel, SVectors(list of ndarray), \ UVectors(list of ndarray) for each channel, \ stachans, List of String (station.channel) .. note:: We recommend that you align the data before computing the \ SVD, e.g., the P-arrival on all templates for the same channel \ should appear at the same time in the trace. See the \ stacking.align_traces function for a way to do this. .. note:: Uses the numpy.linalg.svd function, their U, s and V are mapped \ to UVectors, SValues and SVectors respectively. Their V (and ours) \ corresponds to V.H. """

    # Convert templates into ndarrays for each channel
    # First find all unique channels:
    stachans = list(set([(tr.stats.station, tr.stats.channel)
                         for st in stream_list for tr in st]))
    stachans.sort()
    # Initialize a list for the output matrices, one matrix per-channel
    svalues = []
    svectors = []
    uvectors = []
    for stachan in stachans:
        lengths = []
        for st in stream_list:
            tr = st.select(station=stachan[0],
                           channel=stachan[1])
            if len(tr) > 0:
                tr = tr[0]
            else:
                warnings.warn('Stream does not contain %s'
                              % '.'.join(list(stachan)))
                continue
            lengths.append(len(tr.data))
        min_length = min(lengths)
        for stream in stream_list:
            chan = stream.select(station=stachan[0],
                                 channel=stachan[1])
            if chan:
                if len(chan[0].data) > min_length:
                    if abs(len(chan[0].data) - min_length) > 0.1 * \
                            chan[0].stats.sampling_rate:
                        raise IndexError('More than 0.1 s length '
                                         'difference, align and fix')
                    warnings.warn('Channels are not equal length, trimming')
                    chan[0].data = chan[0].data[0:min_length]
                if 'chan_mat' not in locals():
                    chan_mat = chan[0].data
                else:
                    chan_mat = np.vstack((chan_mat, chan[0].data))
        if not len(chan_mat.shape) > 1:
            warnings.warn('Matrix of traces is less than 2D for %s'
                          % '.'.join(list(stachan)))
            continue
        # Be sure to transpose chan_mat as waveforms must define columns
        chan_mat = np.asarray(chan_mat)
        u, s, v = np.linalg.svd(chan_mat.T, full_matrices=full)
        svalues.append(s)
        svectors.append(v)
        uvectors.append(u)
        del (chan_mat)
    return uvectors, svalues, svectors, stachans