plot.py 文件源码

def histogramnd(ax, data, **kwargs):
        """n-dimensional histogram seaborn based
        """
        scatter_data_raw  = data
        scatter_data_cols = ["x_%d" % (i,) for i in range(data.shape[1])]

        # prepare dataframe
        df = pd.DataFrame(scatter_data_raw, columns=scatter_data_cols)

        g = sns.PairGrid(df)
        # g.map_diag(plt.hist)
        g.map_diag(sns.kdeplot)
        g.map_offdiag(plt.hexbin, cmap="gray", gridsize=30, bins="log");

        # logger.log(loglevel_debug, "dir(g)", dir(g))
        # print g.diag_axes
        # print g.axes

        # for i in range(data.shape[1]):
        #     for j in range(data.shape[1]): # 1, 2; 0, 2; 0, 1
        #         if i == j:
        #             continue
        #         # column gives x axis, row gives y axis, thus need to reverse the selection for plotting goal
        #         # g.axes[i,j].plot(df["%s%d" % (self.cols_goal_base, j)], df["%s%d" % (self.cols_goal_base, i)], "ro", alpha=0.5)
        #         g.axes[i,j].plot(df["x_%d" % (j,)], df["x_%d" % (i,)], "ro", alpha=0.5)

        plt.show()


        # run sns scattermatrix on dataframe
        # plot_scattermatrix(df, ax = None)