def analyze(self, count):
        """ Analyze count data from :meth:`PDFHistogram.count`.

        Turns an array of counts (see :meth:`PDFHistogram.count`) into a
        histogram of probabilities, and  estimates the mean, standard
        deviation, and other statistical characteristics of the corresponding
        probability distribution.

        Args:
            count (array): Array of length ``nbin+2`` containing histogram
                data where ``count[0]`` is the count for values that are
                below the range of the histogram, ``count[-1]`` is the count
                for values above the range, and ``count[i]`` is the count
                for the ``i``-th bin where ``i=1...nbin``.

        Returns a named tuple containing the following information (in order):

            *bins*: Array of bin edges for histogram (length ``nbin+1``)

            *prob*: Array of probabilities for each bin.

            *stats*: Statistical data about histogram. See :class:`PDFStatistics`.

            *norm*: Convert counts into probabilities by dividing by ``norm``.
        """
        if numpy.ndim(count) != 1:
            raise ValueError('count must have dimension 1')
        if len(count) == len(self.midpoints) + 2:
            norm = numpy.sum(count)
            data = numpy.asarray(count[1:-1]) / norm
        elif len(count) != len(self.midpoints):
            raise ValueError(
                'wrong data length: %s != %s'
                    % (len(count), len(self.midpoints))
                )
        else:
            data = count
            norm = 1.
        mid = self.midpoints
        stats = PDFStatistics(histogram=(self.bins, count))
        return PDFHistogram.Histogram(self.bins, data, stats, norm)
Analyze count data from :meth:`PDFHistogram.count`.

        Turns an array of counts (see :meth:`PDFHistogram.count`) into a
        histogram of probabilities, and  estimates the mean, standard
        deviation, and other statistical characteristics of the corresponding
        probability distribution.

        Args:
            count (array): Array of length ``nbin+2`` containing histogram
                data where ``count[0]`` is the count for values that are
                below the range of the histogram, ``count[-1]`` is the count
                for values above the range, and ``count[i]`` is the count
                for the ``i``-th bin where ``i=1...nbin``.

        Returns a named tuple containing the following information (in order):

            *bins*: Array of bin edges for histogram (length ``nbin+1``)

            *prob*: Array of probabilities for each bin.

            *stats*: Statistical data about histogram. See :class:`PDFStatistics`.

            *norm*: Convert counts into probabilities by dividing by ``norm``.