old_contents: """
  This example shows how to plot data on rectangular 2D grids
  (grids that are not rectlinear in geographic or native map projection
  coordinates).
  
  An example of such a grid is the 'POP' grid which is used in
  the ocean component NCAR Community Climate System Model (CCSM).
  "POP" stands for "Parallel Ocean Program", which was developed
  at Los Alamos.
  
  These grids may be thought of as rectangular arrays wrapped around the
  globe in the usual way, with one subscript, call it I, associated with
  longitude and the other subscript, call it J, associated with latitude,
  and then deformed in such a way as to move the top edge of the array to
  a circle centered somewhere other than over the North Pole (typically,
  over Greenland or Canada) and the bottom edge of the array to a circle
  that is centered on the South Pole, but lies entirely within Antarctica.
  The lines defined by the rows and columns of the rectangular arrays are
  locally orthogonal to each other.
  
  POP grids are used extensively locally in oceanographic and ice models.
  """
  from matplotlib import rcParams
  import numpy.ma as ma
  import numpy as np
  import matplotlib.pyplot as plt
  from mpl_toolkits.basemap import Basemap, NetCDFFile
  
  # read in data from netCDF file.
  infile    = 'ccsm_popgrid.nc'
  fpin      = NetCDFFile(infile)
  tlat      = fpin.variables['TLAT'][:]
  tlon      = fpin.variables['TLONG'][:]
  # masked array returned, masked where data == _FillValue
  temp      = fpin.variables['TEMP'][:]
  fpin.close()
  
  # make longitudes monotonically increasing.
  tlon = np.where(np.greater_equal(tlon,min(tlon[:,0])),tlon-360,tlon)
  
  # stack grids side-by-side (in longitiudinal direction), so
  # any range of longitudes may be plotted on a world map.
  tlon = np.concatenate((tlon,tlon+360),1)
  tlat = np.concatenate((tlat,tlat),1)
  temp = ma.concatenate((temp,temp),1)
  tlon = tlon-360.
  
  plt.figure(figsize=(6,8))
  plt.subplot(2,1,1)
  # subplot 1 just shows POP grid cells.
  map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
        urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
  
  map.drawcoastlines()
  map.fillcontinents(color='white')
  
  x, y = map(tlon,tlat)
  im = map.pcolor(x,y,ma.masked_array(np.zeros(temp.shape,'f'), temp.mask),\
                  shading='faceted',cmap=plt.cm.cool,vmin=0,vmax=0)
  # disclaimer:  these are not really the grid cells because of the
  # way pcolor interprets the x and y args.
  plt.title('(A) CCSM POP Grid Cells')
  
  # subplot 2 is a contour plot of surface temperature from the
  # CCSM ocean model.
  plt.subplot(2,1,2)
  map.drawcoastlines()
  map.fillcontinents(color='white')
  
  CS1 = map.contourf(x,y,temp,15)
  CS2 = map.contour(x,y,temp,15,colors='black',linewidths=0.5)
  plt.title('(B) Surface Temp contours on POP Grid')
  
  plt.show()
  #plt.savefig('ccsm_popgrid.ps')
  
  
new_contents: """
  This example shows how to plot data on rectangular 2D grids
  (grids that are not rectlinear in geographic or native map projection
  coordinates).
  
  An example of such a grid is the 'POP' grid which is used in
  the ocean component NCAR Community Climate System Model (CCSM).
  "POP" stands for "Parallel Ocean Program", which was developed
  at Los Alamos.
  
  These grids may be thought of as rectangular arrays wrapped around the
  globe in the usual way, with one subscript, call it I, associated with
  longitude and the other subscript, call it J, associated with latitude,
  and then deformed in such a way as to move the top edge of the array to
  a circle centered somewhere other than over the North Pole (typically,
  over Greenland or Canada) and the bottom edge of the array to a circle
  that is centered on the South Pole, but lies entirely within Antarctica.
  The lines defined by the rows and columns of the rectangular arrays are
  locally orthogonal to each other.
  
  POP grids are used extensively locally in oceanographic and ice models.
  """
  from matplotlib import rcParams
  import numpy.ma as ma
  import numpy as np
  import matplotlib.pyplot as plt
  from mpl_toolkits.basemap import Basemap, NetCDFFile
  
  # read in data from netCDF file.
  infile    = 'ccsm_popgrid.nc'
  fpin      = NetCDFFile(infile)
  tlat      = fpin.variables['TLAT'][:]
  tlon      = fpin.variables['TLONG'][:]
  # masked array returned, masked where data == _FillValue
  temp      = fpin.variables['TEMP'][:]
  fpin.close()
  
  # make longitudes monotonically increasing.
  tlon = np.where(np.greater_equal(tlon,min(tlon[:,0])),tlon-360,tlon)
  
  # stack grids side-by-side (in longitiudinal direction), so
  # any range of longitudes may be plotted on a world map.
  tlon = np.concatenate((tlon,tlon+360),1)
  tlat = np.concatenate((tlat,tlat),1)
  temp = ma.concatenate((temp,temp),1)
  tlon = tlon-360.
  
  plt.figure(figsize=(6,8))
  plt.subplot(2,1,1)
  # subplot 1 just shows POP grid cells.
  map = Basemap(projection='merc', lat_ts=20, llcrnrlon=-180, \
        urcrnrlon=180, llcrnrlat=-84, urcrnrlat=84, resolution='c')
  
  map.drawcoastlines()
  map.fillcontinents(color='white')
  
  x, y = map(tlon,tlat)
  im = map.pcolor(x,y,ma.masked_array(np.zeros(temp.shape,'f'), temp.mask),
                  shading='faceted', antialiased=True, cmap=plt.cm.cool, 
                  vmin=0, vmax=0)
  # disclaimer:  these are not really the grid cells because of the
  # way pcolor interprets the x and y args.
  plt.title('(A) CCSM POP Grid Cells')
  
  # subplot 2 is a contour plot of surface temperature from the
  # CCSM ocean model.
  plt.subplot(2,1,2)
  map.drawcoastlines()
  map.fillcontinents(color='white')
  
  CS1 = map.contourf(x,y,temp,15)
  CS2 = map.contour(x,y,temp,15,colors='black',linewidths=0.5)
  plt.title('(B) Surface Temp contours on POP Grid')
  
  plt.show()
  #plt.savefig('ccsm_popgrid.ps')
  
  
subject: add antialiased=True to pcolor call (SF bugs item #3019150)