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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 quantile_1D(data, weights, quantile):
 """ Compute the weighted quantile of a 1D numpy array. Parameters data : ndarray Input array (one dimension). weights : ndarray Array with the weights of the same size of `data`. quantile : float Quantile to compute. It must have a value between 0 and 1. Returns ------- quantile_1D : float The output value. """

    # Check the data
    if not isinstance(data, np.matrix):
        data = np.asarray(data)
    if not isinstance(weights, np.matrix):
        weights = np.asarray(weights)
    nd = data.ndim
    if nd != 1:
        raise TypeError("data must be a one dimensional array")
    ndw = weights.ndim
    if ndw != 1:
        raise TypeError("weights must be a one dimensional array")
    if data.shape != weights.shape:
        raise TypeError("the length of data and weights must be the same")
    if ((quantile > 1.) or (quantile < 0.)):
        raise ValueError("quantile must have a value between 0. and 1.")
    # Sort the data
    ind_sorted = np.argsort(data)
    sorted_data = data[ind_sorted]
    sorted_weights = weights[ind_sorted]
    # Compute the auxiliary arrays
    Sn = np.cumsum(sorted_weights)
    # TODO: Check that the weights do not sum zero
    #assert Sn != 0, "The sum of the weights must not be zero"
    Pn = (Sn-0.5*sorted_weights)/Sn[-1]
    # Get the value of the weighted median
    return np.interp(quantile, Pn, sorted_data)