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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 find_fisher_stats(*args, **kwargs):
 """ Returns the mean vector and summary statistics for a set of measurements. By default, this expects the input to be plunges and bearings, but the type of input can be controlled through the ``measurement`` kwarg. Parameters *args : 2 or 3 sequences of measurements By default, this will be expected to be ``plunge`` & ``bearing``, both array-like sequences representing linear features. (Rake measurements require three parameters, thus the variable number of arguments.) The *measurement* kwarg controls how these arguments are interpreted. conf : number The confidence level (0-100). Defaults to 95%, similar to 2 sigma. measurement : string, optional Controls how the input arguments are interpreted. Defaults to ``"lines"``. May be one of the following: ``"poles"`` : strikes, dips Arguments are assumed to be sequences of strikes and dips of planes. Poles to these planes are used for analysis. ``"lines"`` : plunges, bearings Arguments are assumed to be sequences of plunges and bearings of linear features. ``"rakes"`` : strikes, dips, rakes Arguments are assumed to be sequences of strikes, dips, and rakes along the plane. ``"radians"`` : lon, lat Arguments are assumed to be "raw" longitudes and latitudes in the stereonet's underlying coordinate system. Returns ------- mean_vector: tuple of two floats A set consisting of the plunge and bearing of the mean vector (in degrees). stats : tuple of three floats ``(r_value, confidence, kappa)`` The ``r_value`` is the magnitude of the mean vector as a number between 0 and 1. The ``confidence`` radius is the opening angle of a small circle that corresponds to the confidence in the calculated direction, and is dependent on the input ``conf``. The ``kappa`` value is the dispersion factor that quantifies the amount of dispersion of the given vectors, analgous to a variance/stddev. """

    # How the heck did this wind up as a separate function?
    lon, lat = _convert_measurements(args, kwargs.get('measurement', 'lines'))
    conf = kwargs.get('conf', 95)
    center, stats = stereonet_math.fisher_stats(lon, lat, conf)
    plunge, bearing = stereonet_math.geographic2plunge_bearing(*center)
    mean_vector = (plunge[0], bearing[0])
    return mean_vector, stats