def df_as_bins(cls, df, gap_file):
'''
converts a already scored df to an object
containing a dict of bins per chromosome, separated by gaps.
'''
chromd = collections.defaultdict(list)
for _, x in df.iterrows():
b = util.bed(x['chrom'], x['start'], x['end'],
x['score'])
chromd[b.chrom].append(b)
return cls.with_unalignable_regions_masked(chromd, gap_file)
def scale_neg_scores(self, scale):
return GenomeBins(
{k:[util.bed(x.chrom, x.start, x.end,
x.score if x.score > 0 else x.score * scale)
for x in xs]
for k, xs in self.chrom_bins.items()})
def chrom_max(chrom):
xs = max_segments(self.chrom_scores[chrom])
bins = self.chrom_bins[chrom]
return [util.bed(bins[x.from_idx].chrom, bins[x.from_idx].start,
bins[x.to_idx].end, x.score) for x in xs]
