How to use the cooltools.lib.numutils.interpolate_bad_singletons function in cooltools

# clip first diagonals and high values
      clipval = np.nanmedian(np.diag(seq_hic_raw,2))
      for i in [-1,0,1]: set_diag(seq_hic_raw,clipval,i)
      seq_hic_raw = np.clip(seq_hic_raw, 0, seq_hic_raw)
      seq_hic_raw[seq_hic_nan] = np.nan

      # adaptively coarsegrain based on raw counts
      seq_hic_smoothed =  adaptive_coarsegrain(
                              seq_hic_raw,  
                              genome_hic_cool.matrix(balance=False).fetch(mseq_str),  
                              cutoff= 2, max_levels=8)
      #todo: pass an option to add a certain pseudocount value, or the minimum nonzero value

      if options.as_obsexp == True:
        # interpolate single missing bins
        seq_hic_interpolated =  interpolate_bad_singletons(seq_hic_smoothed, mask=(~seq_hic_nan),
                                                 fillDiagonal=True, returnMask=False, secondPass=True,verbose=False)
        seq_hic_nan = np.isnan(seq_hic_interpolated)

        # compute observed/expected
        seq_hic_obsexp = observed_over_expected(seq_hic_interpolated, ~seq_hic_nan)[0]
        # todo: allow passing a global expected rather than computing locally

        # log
        seq_hic_obsexp = np.log(seq_hic_obsexp)

        # set nan to 0
        seq_hic_obsexp = np.nan_to_num(seq_hic_obsexp)

        # todo: make obsexp_clip an option for obs/exp
        seq_hic = np.clip(seq_hic_obsexp,-2,2)