docs: def corr_vs_pval(r, pval, plim=0.01, rlim=0.4, dpi=96):
      """Histogram for correlation coefficients and its p-values (colored)
  
      Parameters:
      -----------
      r : np.ndarray
          Correlation coefficient matrix.
          The upper triangular elements are extracted if a NxN is provided.
          Otherwise provide a plain vector.
  
      pval : np.ndarray
          NxN matrix with p-values
  
      plim : float
          Discretionary alpha threshold to judge if a p-value is considered
          "significant" or not. (Default: 0.01 or 1%)
  
      rlim : float
          Descretionary threshold to judge if an absolute correlation
          coefficient is big enough. (Default: 0.4)
  
      dpi : int
          Set the resolution of the matplotlib graphics.
  
      Return:
      -------
      fig, ax
          matplotlib figure and axes for further tweaking
      """
      # reshape
      if len(r.shape) == 2:
          idx = (np.tri(N=r.shape[0], k=-1) == 1)
          r = r[idx]
          pval = pval[idx]
  
      # indicies for the three groups
      i1 = (pval >= plim)
      i2 = (pval < plim) & (np.abs(r) > rlim)
      i3 = (pval < plim) & (np.abs(r) <= rlim)
  
      # plot paramters
      absmax = np.max(np.abs(r))
      b = (np.arange(0, 21) / 10 - 1) * absmax
      c = plt.get_cmap('tab10').colors
      c = (c[1], c[8], c[7])
  
      # create plot
      fig, ax = plt.subplots(dpi=dpi)
      ax.hist((r[i1], r[i2], r[i3]), histtype='bar',
              stacked=True, bins=b, color=c)
  
      # legend, title, labels
      ax.legend(['p >= ' + str(plim),
                 'p < ' + str(plim) + ' and |r| > ' + str(rlim),
                 'p < ' + str(plim) + ' and |r| <= ' + str(rlim)],
                loc=2, bbox_to_anchor=(1.04, 1.04))
      ax.set_ylabel('frequency')
      ax.set_xlabel('correlation coefficient')
  
      # design grid
      ax.grid(color='darkgray', linestyle='-.')
      for edge, spine in ax.spines.items():
          spine.set_visible(False)
  
      # output figure/axes onbjects
      return fig, ax