<SYSTEM_TASK:>
Solve the following problem using Python, implementing the functions described below, one line at a time
<END_TASK>
<USER_TASK:>
Description:
 def calculate_metric(Js, logJs, loglogJs, logloglogJs, loglogloglogJs, \ mapping):
 """ This function will take the various integrals calculated by get_moments and convert this into a metric for the appropriate parameter space. Parameters Js : Dictionary The list of (log^0 x) * x**(-i/3) integrals computed by get_moments() The index is Js[i] logJs : Dictionary The list of (log^1 x) * x**(-i/3) integrals computed by get_moments() The index is logJs[i] loglogJs : Dictionary The list of (log^2 x) * x**(-i/3) integrals computed by get_moments() The index is loglogJs[i] logloglogJs : Dictionary The list of (log^3 x) * x**(-i/3) integrals computed by get_moments() The index is logloglogJs[i] loglogloglogJs : Dictionary The list of (log^4 x) * x**(-i/3) integrals computed by get_moments() The index is loglogloglogJs[i] mapping : dictionary Used to identify which Lambda components are active in this parameter space and map these to entries in the metric matrix. Returns -------- metric : numpy.matrix The resulting metric. """


    # How many dimensions in the parameter space?
    maxLen = len(mapping.keys())

    metric = numpy.matrix(numpy.zeros(shape=(maxLen,maxLen),dtype=float))
    unmax_metric = numpy.matrix(numpy.zeros(shape=(maxLen+1,maxLen+1),
                                                                  dtype=float))

    for i in range(16):
        for j in range(16):
            calculate_metric_comp(metric, unmax_metric, i, j, Js,
                                           logJs, loglogJs, logloglogJs,
                                           loglogloglogJs, mapping)
    return metric, unmax_metric