old_contents: # TODO : populate after development is done
  from nilearn import plotting as nplt
  from .utils import unmask_img
  from numpy import allclose
  from scipy.spatial.distance import squareform
  from scipy.stats import rankdata
  from matplotlib.pyplot import colorbar, imshow
  from sklearn.preprocessing import minmax_scale
  
  def plot_anat(anat_img, **kwargs):
      nplt.plot_anat(anat_img, **kwargs)
      return
  
  
  def plot_epi(epi_img, **kwargs):
      nplt.plot_epi(epi_img, **kwargs)
      return
  
  
  def plot_stat_map(stat_img, bg_img, **kwargs):
      nplt.plot_stat_map(stat_img, bg_img, **kwargs)
      return
  
  
  def plot_roi(roi, bg, **kwargs):
      nplt.plot_roi(roi, bg, **kwargs)
      return
  
  
  def plot_connectome(mat, coords, **kwargs):
      nplt.plot_connectome(mat, coords, **kwargs)
      return
  
  
  def plot_masked(mat, mask, **kwargs):
      nplt.plot_stat_map(unmask_img(mat, mask), **kwargs)
      return
  
  
  def plot_rdm(rdm, rank=True, scale=True, ax=None, cb=True, **plot_args):
      # for now, can only pass in a vector or square matrix (allclose might break).
      # TODO : fix error handling
      if rdm.ndim > 1:
          if allclose(rdm, rdm.T):
              rdm = squareform(rdm)  # transform to vector if square
  
      if rank:
          rdm = rankdata(rdm)
  
      if scale:
          rdm = minmax_scale(rdm)
  
      rdm = squareform(rdm)
  
      if ax is not None:
          im = ax.imshow(rdm, **plot_args)
      else:
          im = imshow(rdm, **plot_args)
  
      if cb:
          colorbar(im, ax=ax)
      return im
  
new_contents: # TODO : populate after development is done
  from nilearn import plotting as nplt
  from .utils import unmask_img
  from numpy import allclose
  from scipy.spatial.distance import squareform
  from scipy.stats import rankdata
  from matplotlib.pyplot import colorbar, imshow
  from sklearn.preprocessing import minmax_scale
  
  def plot_anat(anat_img, **kwargs):
      nplt.plot_anat(anat_img, **kwargs)
      return
  
  
  def plot_epi(epi_img, **kwargs):
      nplt.plot_epi(epi_img, **kwargs)
      return
  
  
  def plot_stat_map(stat_img, bg_img, **kwargs):
      nplt.plot_stat_map(stat_img, bg_img, **kwargs)
      return
  
  
  def plot_roi(roi, bg, **kwargs):
      nplt.plot_roi(roi, bg, **kwargs)
      return
  
  
  def plot_connectome(mat, coords, **kwargs):
      nplt.plot_connectome(mat, coords, **kwargs)
      return
  
  
  def plot_masked(mat, mask, **kwargs):
      nplt.plot_stat_map(unmask_img(mat, mask), **kwargs)
      return
  
  
  def plot_rdm(rdm, rank=True, scale=True, ax=None, cb=True, **plot_args):
      # for now, can only pass in a vector or square matrix (allclose might break).
      # TODO : fix error handling
      if rdm.ndim > 1:
          if allclose(rdm, rdm.T):
              rdm = squareform(rdm, checks=False)  # transform to vector if square
  
      if rank:
          rdm = rankdata(rdm)
  
      if scale:
          rdm = minmax_scale(rdm)
  
      rdm = squareform(rdm)
  
      if ax is not None:
          im = ax.imshow(rdm, **plot_args)
      else:
          im = imshow(rdm, **plot_args)
  
      if cb:
          colorbar(im, ax=ax)
      return im
  
subject: fix rdm visualization