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

# 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
        seq_hic = 100000* seq_hic_interpolated

        # todo add extra smoothing

    except ValueError:
      print("WARNING: %s doesn't see %s. Setting to all zeros." % (genome_hic_file, mseq_str))
      seq_hic = np.zeros((seq_len_pool,seq_len_pool), dtype='float16')
    
    # write
    seqs_hic_open['seqs_hic'][si,:,:] = seq_hic.astype('float16')
    
  # close sequences coverage file
  seqs_hic_open.close()

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:
          seq_hic = seq_hic_obsexp

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

        # rescale, reclip
        seq_hic = 100000*seq_hic_interpolated
        clipval = np.nanmedian(np.diag(seq_hic,options.diagonal_offset))
        for i in range(-options.diagonal_offset+1, options.diagonal_offset):
          set_diag(seq_hic,clipval,i)
        seq_hic = np.clip(seq_hic, 0, clipval)

        #extra smoothing. todo pass kernel specs
        if kernel is not None:
          seq_hic = convolve(seq_hic, kernel)

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

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:
          seq_hic = seq_hic_obsexp

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