docs: def colormapped_bedfile(self, genome, cmap=None):
          """
          Create a BED file with padj encoded as color
  
          Features will be colored according to adjusted pval (phred
          transformed).  Downregulated features have the sign flipped.
  
          Parameters
          ----------
          cmap : matplotlib colormap
              Default is matplotlib.cm.RdBu_r
  
          Notes
          -----
          Requires a FeatureDB to be attached.
          """
          if self.db is None:
              raise ValueError("FeatureDB required")
          db = gffutils.FeatureDB(self.db)
  
          def scored_feature_generator(d):
              for i in range(len(d)):
                  try:
                      feature = db[d.ix[i]]
                  except gffutils.FeatureNotFoundError:
                      raise gffutils.FeatureNotFoundError(d.ix[i])
                  score = -10 * np.log10(d.padj[i])
                  lfc = d.log2FoldChange[i]
                  if np.isnan(lfc):
                      score = 0
                  if lfc < 0:
                      score *= -1
                  feature.score = str(score)
                  feature = extend_fields(
                      gff2bed(gffutils.helpers.asinterval(feature)), 9)
                  fields = feature.fields[:]
                  fields[6] = fields[1]
                  fields[7] = fields[2]
                  fields.append(str(d.padj[i]))
                  fields.append(str(d.pval[i]))
                  fields.append('%.3f' % d.log2FoldChange[i])
                  fields.append('%.3f' % d.baseMeanB[i])
                  fields.append('%.3f' % d.baseMeanB[i])
                  yield pybedtools.create_interval_from_list(fields)
  
          x = pybedtools.BedTool(scored_feature_generator(self)).saveas()
          norm = x.colormap_normalize()
          if cmap is None:
              cmap = cm.RdBu_r
          cmap = colormap_adjust.cmap_center_point_adjust(
              cmap, [norm.vmin, norm.vmax], 0)
  
          def score_zeroer(f):
              f.score = '0'
              return f
          return x.each(add_color, cmap=cmap, norm=norm)\
                  .sort()\
                  .each(score_zeroer)\
                  .truncate_to_chrom(genome)\
                  .saveas()