How to use the bioframe._process.run function in bioframe

def _read_bigwig_as_wig(filepath, chrom, start=None, end=None, cachedir=None):
    # https://sebastienvigneau.wordpress.com/2014/01/10/bigwig-to-bedgraph-to-wig/
    # http://redmine.soe.ucsc.edu/forum/index.php?t=msg&goto=5492&S=2925a24be1c20bb064fc09bd054f862d
    cmd = ['bigWigToWig',
           '-chrom={}'.format(chrom)]
    if start is not None:
        cmd += ['-start={}'.format(start)]
    if end is not None:
        cmd += ['-end={}'.format(end)]
    if cachedir is not None:
        cmd += ['-udcDir={}'.format(cachedir)]

    with tempfile.NamedTemporaryFile('w+t') as fh:
        cmd += [filepath, fh.name]
        run(cmd, raises=True)
        fh.flush()

        trackline = fh.readline().split()
        if trackline[0] == '#bedGraph':
            info = {'type': 'bedGraph'}
            out = pd.read_csv(fh, sep='\t', names=['chrom', 'start', 'end', 'value'])
        else:
            tracktype = trackline[0]
            info = dict([kv.split('=') for kv in trackline[1:]])
            info['type'] = tracktype
            for key in ['start', 'step', 'span']:
                if key in info: info[key] = int(info[key])
            if tracktype == 'fixedStep':
                out = pd.read_csv(fh, sep='\t', names=['value'])
            else:
                out = pd.read_csv(fh, sep='\t', names=['start', 'value'])

aggfunc : str, optional
        Aggregation method (summary statistic). One of:
        'mean' - average value in region (default)
        'std' - standard deviation in region
        'min' - minimum value in region
        'max' - maximum value in region
        'coverage' - % of region that is covered

    """
    cmd = ['bigWigSummary',
           filepath, chrom, str(start+1), str(end), str(nbins)]
    if aggfunc is not None:
        cmd += ['-type={}'.format(aggfunc)]
    if cachedir is not None:
        cmd += ['-udcDir={}'.format(cachedir)]
    out = run(cmd, raises=True)
    return pd.read_csv(io.StringIO(out), sep='\t', na_values='n/a', header=None).iloc[0].values

elif isinstance(kwargs[arg], pd.DataFrame):
                    tmp_file = tsv(kwargs[arg])
                    pandas_inputs[arg] = tmp_file
                    kwargs[arg] = tmp_file.name

            cmd = ['bedtools', name]
            for k, v in kwargs.items():
                if isinstance(v, bool):
                    if not v: continue
                    cmd.append('-{}'.format(k))
                else:
                    cmd.append('-{}'.format(k))
                    cmd.append(str(v))

            try:
                out = run(cmd, **run_kws)
            finally:
                for tmp_file in pandas_inputs.values():
                    tmp_file.close()

            if not len(out):
                return pd.DataFrame(columns=columns)
            return to_dataframe(out, columns=columns)

with tempfile.NamedTemporaryFile(suffix='.bg') as f, \
         tempfile.NamedTemporaryFile('wt', suffix='.chrom.sizes') as cs:

        chromsizes.to_csv(cs, sep='\t', header=False)
        cs.flush()

        bg.to_csv(
            f.name,
            sep='\t',
            columns=columns,
            index=False,
            header=False,
            na_rep='nan')

        run(['bedGraphToBigWig', f.name, cs.name, outpath],
            print_cmd=True)