def plot(self, data, coords, msz0, msz1, colormap, dlen, dim, rows, cols):
        for i in range(dim):
            plt.subplot(rows, cols, i+1)

            # This uses the colors uniquely for each record, while in normal views, it is based on the values
            # within each dimensions. This is important when we are dealing with time series. Where we don't want
            # to normalize colors within each time period, rather we like to see the patterns of each data
            # records in time.
            mn = np.min(data[:, :], axis=1)
            mx = np.max(data[:, :], axis=1)

            for j in range(dlen):
                sc = plt.scatter(coords[j, 1],
                                 msz0-1-coords[j, 0],
                                 c=data[j, i],
                                 vmax=mx[j], vmin=mn[j],
                                 s=90,
                                 marker='.',
                                 edgecolor='None',
                                 cmap=colormap,
                                 alpha=1)

            eps = .0075
            plt.xlim(0-eps, msz1-1+eps)
            plt.ylim(0-eps, msz0-1+eps)
            plt.xticks([])
            plt.yticks([])
dataset/ETHPy150Open sevamoo/SOMPY/visualization/dotmap.py/DotMapView.plot/original