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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 p2x(self, p):
 """ Map parameters ``p`` to vector in x-space. x-space is a vector space of dimension ``p.size``. Its axes are in the directions specified by the eigenvectors of ``p``'s covariance matrix, and distance along an axis is in units of the standard deviation in that direction. """

        if hasattr(p, 'keys'):
            dp = BufferDict(p, keys=self.g.keys())._buf[:self.meanflat.size] - self.meanflat
        else:
            dp = numpy.asarray(p).reshape(-1) - self.meanflat
        return self.vec_isig.dot(dp)