code: def corrmat(
      corr,
      figsize=(11, 9),
      annotate=True,
      inflate=True,
      linewidths=0.5,
      cbar_kws="default",
      show=True,
      ax=None,
      title="Correlation Matrix",
      title_font_size=14,
      **kwargs,
  ):
      """Function for generating color-coded correlation triangle.
  
      Parameters
      ----------
      corr : array-like of shape = [n_features, n_features]
          Input correlation matrix. Pass a pandas ``DataFrame`` for axis labels.
  
      figsize : tuple (default = (11, 9))
          Size of printed figure.
  
      annotate : bool (default = True)
          Whether to print the correlation coefficients.
  
      inflate : bool (default = True)
          Whether to inflate correlation coefficients to a 0-100 scale.
          Avoids decimal points in the figure, which often appears very cluttered
          otherwise.
  
      linewidths : float
          with of line separating each coordinate square.
  
      cbar_kws : dict, str (default = 'default')
          Optional arguments to color bar. The default options, 'default',
          passes the ``shrink`` parameter to fit colorbar standard figure frame.
  
      show : bool (default = True)
          whether to print figure using :obj:`matplotlib.pyplot.show`.
  
      title : str
          figure title if shown.
  
      title_font_size : int
          title font size.
  
      ax : object, optional
          axis to attach plot to.
  
      **kwargs : optional
          Other optional arguments to sns heatmap.
  
      Returns
      -------
      ax : object
          axis object.
  
      See Also
      --------
      :class:`mlens.visualization.clustered_corrmap`
      """
      if inflate:
          corr *= 100
          fmt = "2.0f"
      else:
          fmt = ".2f"
  
      if cbar_kws == "default":
          cbar_kws = {"shrink": 1.0}
  
      # Determine annotation
      do_annot = {True: corr, False: None}
      annot = do_annot[annotate]
  
      # Generate a mask for the upper triangle
      mask = np.zeros_like(corr, dtype=np.bool)
      mask[np.triu_indices_from(mask)] = True
  
      # Set up the matplotlib figure
      if ax is None:
          _, ax = plt.subplots(figsize=figsize)
  
      # Generate a custom diverging colormap
      cmap = diverging_palette(220, 10, as_cmap=True)
  
      # Draw the heatmap with the mask and correct aspect ratio
      ax = heatmap(
          corr,
          mask=mask,
          cmap=cmap,
          vmin=corr.min().min(),
          annot=annot,
          fmt=fmt,
          vmax=corr.max().max(),
          square=True,
          linewidths=linewidths,
          cbar_kws=cbar_kws,
          ax=ax,
          **kwargs,
      )
  
      if show:
          plt.title(title, fontsize=title_font_size)
          plt.show()
  
      return ax
  
imports: from __future__ import division, print_function
  import numpy
  from scipy.stats import pearsonr
  import warnings
label_desc: Use image node_id 1 for calling a global function with example usage: corrmat(corr, figsize, annotate, inflate, linewidths, cbar_kws, show, ax, title, title_font_size) and returns: ax