def topic_hist(self, topic_indices=None, d_indices=[], show=True):
        import matplotlib.pyplot as plt

        if len(d_indices) == 0:
            i = self.corpus.context_types.index(self.model.context_type)
            d_indices = xrange(len(self.corpus.context_data[i]))

        td_mat = self.model.top_doc[:, d_indices]
        td_mat /= td_mat.sum(0)
        arr = td_mat.sum(1)

        if not topic_indices == None:
            arr = arr[topic_indices]

        l = enum_sort(arr)
        rank, prob = zip(*l)

        y_pos = np.arange(len(rank))
        fig = plt.figure(figsize=(10,10))

        plt.barh(y_pos, list(prob))
        plt.yticks(y_pos, rank)
        plt.xlabel('Frequencies')
        plt.title('Topic Proportions')

        if show:
            plt.show()

        return plt
Draws a histogram showing the proportion of topics within a set of
dataset/ETHPy150Open inpho/vsm/vsm/viewer/ldacgsviewer.py/LdaCgsViewer.topic_hist