new_contents: import numpy as np
  import matplotlib.pyplot as plt
  import matplotlib.colors as colors
  import xgcm
  import cartopy.crs as ccrs
  
  from xmitgcm import open_mdsdataset
  from matplotlib.mlab import bivariate_normal
  from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
  
  dir0 = '/homedata/bderembl/runmit/test_southatlgyre3'
  
  ds0 = open_mdsdataset(dir0,iters='all',prefix=['U','V'])
  
  grid = xgcm.Grid(ds0)
  print(grid)
  
  Vorticity = (-grid.diff(ds0.U.where(ds0.hFacW>0)*ds0.dxC, 'Y') + grid.diff(ds0.V.where(ds0.hFacS>0)*ds0.dyC, 'X'))/ds0.rAz
  print('Vorticity')
  
  i = 0
  nz = 0
  
  
  while (i < 150) :
      i=i+1
      print(i)
      plt.figure(1)
      ax = plt.subplot(projection=ccrs.PlateCarree());
      Vorticity[i,nz,:,:].plot.pcolormesh('XG','YG', ax=ax,vmin=-0.00020,vmax=0.00020,cmap='ocean')
      plt.title('Case 4 : Vorticity')
      plt.text(5,5,i,ha='center',wrap=True)
      ax.coastlines()
      gl = ax.gridlines(draw_labels=True, alpha = 0.5, linestyle='--');
      gl.xlabels_top = False
      gl.ylabels_right = False
      gl.xformatter = LONGITUDE_FORMATTER
      gl.yformatter = LATITUDE_FORMATTER
      if (i < 10):
          plt.savefig('Vorticity_cas4-'+'00'+str(i)+'.png')
          plt.clf()
      elif (i > 9) and (i < 100):
          plt.savefig('Vorticity_cas4-'+'0'+str(i)+'.png')
          plt.clf()
      else:
          plt.savefig('Vorticity_cas4-'+str(i)+'.png')
          plt.clf()
  
subject: Create A_Vorticity.py