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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 generate_overlapping_psds(opt, gwstrain, flen, delta_f, flow, dyn_range_factor=1., precision=None):
 """Generate a set of overlapping PSDs to cover a stretch of data. This allows one to analyse a long stretch of data with PSD measurements that change with time. Parameters opt : object Result of parsing the CLI with OptionParser, or any object with the required attributes (psd_model, psd_file, asd_file, psd_estimation, psd_segment_length, psd_segment_stride, psd_inverse_length, psd_output). gwstrain : Strain object The timeseries of raw data on which to estimate PSDs. flen : int The length in samples of the output PSDs. delta_f : float The frequency step of the output PSDs. flow: float The low frequncy cutoff to use when calculating the PSD. dyn_range_factor : {1, float} For PSDs taken from models or text files, if `dyn_range_factor` is not None, then the PSD is multiplied by `dyn_range_factor` ** 2. precision : str, choices (None,'single','double') If not specified, or specified as None, the precision of the returned PSD will match the precision of the data, if measuring a PSD, or will match the default precision of the model if using an analytical PSD. If 'single' the PSD will be converted to float32, if not already in that precision. If 'double' the PSD will be converted to float64, if not already in that precision. Returns -------- psd_and_times : list of (start, end, PSD) tuples This is a list of tuples containing one entry for each PSD. The first and second entries (start, end) in each tuple represent the index range of the gwstrain data that was used to estimate that PSD. The third entry (psd) contains the PSD estimate between that interval. """

    if not opt.psd_estimation:
        psd = from_cli(opt, flen, delta_f, flow, strain=gwstrain,
                       dyn_range_factor=dyn_range_factor, precision=precision)
        psds_and_times = [ (0, len(gwstrain), psd) ]
        return psds_and_times

    # Figure out the data length used for PSD generation
    seg_stride = int(opt.psd_segment_stride * gwstrain.sample_rate)
    seg_len = int(opt.psd_segment_length * gwstrain.sample_rate)
    input_data_len = len(gwstrain)

    if opt.psd_num_segments is None:
        # FIXME: Should we make --psd-num-segments mandatory?
        #        err_msg = "You must supply --num-segments."
        #        raise ValueError(err_msg)
        num_segments = int(input_data_len // seg_stride) - 1
    else:
        num_segments = int(opt.psd_num_segments)

    psd_data_len = (num_segments - 1) * seg_stride + seg_len

    # How many unique PSD measurements is this?
    psds_and_times = []
    if input_data_len < psd_data_len:
        err_msg = "Input data length must be longer than data length needed "
        err_msg += "to estimate a PSD. You specified that a PSD should be "
        err_msg += "estimated with %d seconds. " %(psd_data_len)
        err_msg += "Input data length is %d seconds. " %(input_data_len)
        raise ValueError(err_msg)
    elif input_data_len == psd_data_len:
        num_psd_measurements = 1
        psd_stride = 0
    else:
        num_psd_measurements = int(2 * (input_data_len-1) / psd_data_len)
        psd_stride = int((input_data_len - psd_data_len) / num_psd_measurements)

    for idx in range(num_psd_measurements):
        if idx == (num_psd_measurements - 1):
            start_idx = input_data_len - psd_data_len
            end_idx = input_data_len
        else:
            start_idx = psd_stride * idx
            end_idx = psd_data_len + psd_stride * idx
        strain_part = gwstrain[start_idx:end_idx]
        psd = from_cli(opt, flen, delta_f, flow, strain=strain_part,
                       dyn_range_factor=dyn_range_factor, precision=precision)
        psds_and_times.append( (start_idx, end_idx, psd) )
    return psds_and_times