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

if options.as_obsexp:
        # compute obs/exp        
        if options.global_obsexp: # compute global obs/exp
          exp_chr = genome_hic_expected.iloc[ genome_hic_expected['chrom'].values ==mseq.chr][0:seq_len_pool]
          if len(exp_chr) ==0: 
              raise ValueError('no expected values found for chr:'+mseq.chr)
          exp_map= np.zeros((seq_len_pool,seq_len_pool))
          for i in range(seq_len_pool):
            set_diag(exp_map,exp_chr['balanced.avg'].values[i],i)
            set_diag(exp_map,exp_chr['balanced.avg'].values[i],-i)
          seq_hic_obsexp = seq_hic_smoothed / exp_map
          for i in range(-options.diagonal_offset+1,options.diagonal_offset): set_diag(seq_hic_obsexp,1.0,i)
          seq_hic_obsexp[seq_hic_nan] = np.nan          

        else: # compute local obs/exp
          seq_hic_obsexp = observed_over_expected(seq_hic_smoothed, ~seq_hic_nan)[0]

        # log
        if options.no_log==False:
          seq_hic_obsexp = np.log(seq_hic_obsexp)
          seq_hic_obsexp = np.clip(seq_hic_obsexp, -options.clip, options.clip)
          seq_hic_obsexp = interp_nan(seq_hic_obsexp)
          for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 0,i)
        else:
          seq_hic_obsexp = np.clip(seq_hic_obsexp, 0, options.clip)
          seq_hic_obsexp = interp_nan(seq_hic_obsexp)
          for i in range(-options.diagonal_offset+1, options.diagonal_offset): set_diag(seq_hic_obsexp, 1,i)

        # apply kernel
        if kernel is not None:
          seq_hic = convolve(seq_hic_obsexp, kernel)
        else:

# 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)
      
      else:
        # interpolate all missing bins
        seq_hic_interpolated =  interp_nan(seq_hic_smoothed)

        # rescale

if chr_pre:
        mseq_str = '%s:%d-%d' % (mseq.chr, mseq.start, mseq.end)
      else:
        mseq_str = '%s:%d-%d' % (mseq.chr[3:], mseq.start, mseq.end)
      seq_hic_raw = genome_hic_cool.matrix(balance=True).fetch(mseq_str)

      # interpolate
      seq_hic_raw = interpolateNearest(seq_hic_raw)

      # find minimum nonzero value
      seq_hic_min = np.min(seq_hic_raw[seq_hic_raw > 0])
      seq_hic_raw += seq_hic_min

      # compute observed/expected
      seq_hic_nan = np.isnan(seq_hic_raw)
      seq_hic_obsexp = observed_over_expected(seq_hic_raw, ~seq_hic_nan)[0]

      # log
      seq_hic_obsexp = np.log(seq_hic_obsexp)

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

    except ValueError:
      print("WARNING: %s doesn't see %s. Setting to all zeros." % (genome_hic_file, mseq_str))
      seq_hic_obsexp = np.zeros((seq_len_pool,seq_len_pool), dtype='float16')

    # write
    seqs_hic_open['seqs_hic'][si,:] = seq_hic_obsexp

  # close sequences coverage file
  seqs_hic_open.close()