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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 gaussian1d_moments(data, mask=None):
 """ Estimate 1D Gaussian parameters from the moments of 1D data. This function can be useful for providing initial parameter values when fitting a 1D Gaussian to the ``data``. Parameters data : array_like (1D) The 1D array. mask : array_like (1D bool), optional A boolean mask, with the same shape as ``data``, where a `True` value indicates the corresponding element of ``data`` is masked. Returns ------- amplitude, mean, stddev : float The estimated parameters of a 1D Gaussian. """


    if np.any(~np.isfinite(data)):
        data = np.ma.masked_invalid(data)
        warnings.warn('Input data contains input values (e.g. NaNs or infs), '
                      'which were automatically masked.', AstropyUserWarning)
    else:
        data = np.ma.array(data)

    if mask is not None and mask is not np.ma.nomask:
        mask = np.asanyarray(mask)
        if data.shape != mask.shape:
            raise ValueError('data and mask must have the same shape.')
        data.mask |= mask

    data.fill_value = 0.
    data = data.filled()

    x = np.arange(data.size)
    x_mean = np.sum(x * data) / np.sum(data)
    x_stddev = np.sqrt(abs(np.sum(data * (x - x_mean)**2) / np.sum(data)))
    amplitude = np.ptp(data)

    return amplitude, x_mean, x_stddev