new_contents: import matplotlib.pyplot as plt
  import matplotlib.colors as colors
  import numpy as np
  
  """
  The NDVI values will range from -1 to 1. You want to use a diverging color scheme to visualize the data,
  and you want to center the colorbar at a defined midpoint. The class below allows you to normalize the colorbar.
  """
  class MidpointNormalize(colors.Normalize):
      """
      Normalise the colorbar so that diverging bars work there way either side from a prescribed midpoint value)
      e.g. im=ax1.imshow(array, norm=MidpointNormalize(midpoint=0.,vmin=-100, vmax=100))
      Credit: Joe Kington, http://chris35wills.github.io/matplotlib_diverging_colorbar/
      Credit: https://stackoverflow.com/a/48598564
      """
      def __init__(self, vmin=None, vmax=None, midpoint=None, clip=False):
          self.midpoint = midpoint
          colors.Normalize.__init__(self, vmin, vmax, clip)
      
      def __call__(self, value, clip=None):    
          # Note that I'm ignoring clipping and other edge cases here.
          result, is_scalar = self.process_value(value)
          x, y = [self.vmin, self.midpoint, self.vmax], [0, 0.5, 1]
          return np.ma.array(np.interp(value, x, y), mask=result.mask, copy=False)
  
  
  def show_ndvi(ndvi, figsize=(20, 10)):
      fig = plt.figure(figsize=figsize)
      ax = fig.add_subplot(111)
  
      # diverging color scheme chosen from https://matplotlib.org/users/colormaps.html
      cmap = plt.cm.RdYlGn 
  
      mmin = np.nanmin(ndvi)
      mmax = np.nanmax(ndvi)
      mid = 0
  
      cax = ax.imshow(ndvi, cmap=cmap, clim=(mmin, mmax),
                      norm=MidpointNormalize(midpoint=mid,vmin=mmin, vmax=mmax))
  
      ax.axis('off')
      ax.set_title('Normalized Difference Vegetation Index', fontsize=18, fontweight='bold')
  
      cbar = fig.colorbar(cax, orientation='horizontal', shrink=0.5)
  
      plt.show()
subject: add visualization module