docs: def find_contours_level(density, x, y, level, closed=False):
      """Find iso-valued density contours for a given level value
  
      Parameters
      ----------
      density: 2d ndarray of shape (M, N)
          Kernel density estimate for which to compute the contours
      x: 2d ndarray of shape (M, N) or 1d ndarray of size M
          X-values corresponding to `kde`
      y: 2d ndarray of shape (M, N) or 1d ndarray of size M
          Y-values corresponding to `kde`
      level: float between 0 and 1
          Value along which to find contours in `kde` relative
          to its maximum kde
  
      Returns
      -------
      contours: list of ndarrays of shape (P, 2)
          Contours found for the given level value
  
      See Also
      --------
      skimage.measure.find_contours: Contour finding algorithm used
      """
      if level >= 1 or level <= 0:
          raise ValueError("`level` must be in (0,1), got '{}'!".format(level))
      # level relative to maximum
      level = level * density.max()
      # xy coordinates
      if len(x.shape) == 2:
          assert np.all(x[:, 0] == x[:, 1])
          x = x[:, 0]
      if len(y.shape) == 2:
          assert np.all(y[0, :] == y[1, :])
          y = y[0, :]
      if closed:
          # find closed contours
          density = np.pad(density, ((1, 1), (1, 1)), mode="constant")
          offset = 1
      else:
          # leave contours open at kde boundary
          offset = 0
  
      conts_idx = find_contours(density, level)
      conts_xy = []
  
      for cc in conts_idx:
          cx = np.interp(x=cc[:, 0]-offset,
                         xp=range(x.size),
                         fp=x)
          cy = np.interp(x=cc[:, 1]-offset,
                         xp=range(y.size),
                         fp=y)
          conts_xy.append(np.stack((cx, cy), axis=1))
  
      return conts_xy