code: def plot_pairwise_ld(m, colorbar=True, ax=None, imshow_kwargs=None):
      
  
      import matplotlib.pyplot as plt
  
      # check inputs
      m_square = ensure_square(m)
  
      # blank out lower triangle and flip up/down
      m_square = np.tril(m_square)[::-1, :]
  
      # set up axes
      if ax is None:
          # make a square figure with enough pixels to represent each variant
          x = m_square.shape[0] / plt.rcParams['figure.dpi']
          x = max(x, plt.rcParams['figure.figsize'][0])
          fig, ax = plt.subplots(figsize=(x, x))
          fig.tight_layout(pad=0)
  
      # setup imshow arguments
      if imshow_kwargs is None:
          imshow_kwargs = dict()
      imshow_kwargs.setdefault('interpolation', 'none')
      imshow_kwargs.setdefault('cmap', 'Greys')
      imshow_kwargs.setdefault('vmin', 0)
      imshow_kwargs.setdefault('vmax', 1)
  
      # plot as image
      im = ax.imshow(m_square, **imshow_kwargs)
  
      # tidy up
      ax.set_xticks([])
      ax.set_yticks([])
      for s in 'bottom', 'right':
          ax.spines[s].set_visible(False)
      if colorbar:
          plt.gcf().colorbar(im, shrink=.5, pad=0)
  
      return ax
docs: Plot a matrix of genotype linkage disequilibrium values between
      all pairs of variants.
  
      Parameters
      ----------
      m : array_like
          Array of linkage disequilibrium values in condensed form.
      colorbar : bool, optional
          If True, add a colorbar to the current figure.
      ax : axes, optional
          The axes on which to draw. If not provided, a new figure will be
          created.
      imshow_kwargs : dict-like, optional
          Additional keyword arguments passed through to
          :func:`matplotlib.pyplot.imshow`.
  
      Returns
      -------
      ax : axes
          The axes on which the plot was drawn.