old_contents: from netCDF4 import Dataset
  from numpy.testing import assert_array_equal, assert_array_almost_equal
  import numpy as np
  import threading
  import Queue
  import time
  
  # demonstrate reading of different files from different threads.
  # Releasing the Global Interpreter Lock (GIL) when calling the
  # netcdf C library for read operations speeds up the reads
  # when threads are used (issue 369).
  # Test script contributed by Ryan May of Unidata.
  
  # Make some files
  nfiles = 4
  fnames = []; datal = []
  for i in range(nfiles):
      fname = 'test%d.nc' % i
      fnames.append(fname)
      nc = Dataset(fname, 'w')
      data = np.random.randn(500, 500, 500)
      datal.append(data)
      nc.createDimension('x', 500)
      nc.createDimension('y', 500)
      nc.createDimension('z', 500)
      var = nc.createVariable('grid', 'f', ('x', 'y', 'z'))
      var[:] = data
      nc.close()
  
  # Queue them up
  items = Queue.Queue()
  for data,fname in zip(datal,fnames):
      items.put(fname)
  
  # Function for threads to use
  def get_data(serial=None):
      if serial is None: # if not called from a thread
          fname = items.get()
      else:
          fname = fnames[serial]
      nc = Dataset(fname, 'r')
      data2 = nc.variables['grid'][:]
      # make sure the data is correct
      assert_array_almost_equal(data2,datal[int(fname[4])])
      nc.close()
      if serial is None:
         items.task_done()
  
  # Time it (no threading).
  start = time.time()
  for i in range(nfiles):
      get_data(serial=i)
  end = time.time()
  print 'no threads, time = ',end - start
  
  # with threading.
  start = time.time()
  for i in range(nfiles):
      threading.Thread(target=get_data).start()
  items.join()
  end = time.time()
  print 'with threading, time = ',end - start
  
new_contents: from netCDF4 import Dataset
  from numpy.testing import assert_array_equal, assert_array_almost_equal
  import numpy as np
  import threading
  import Queue
  import time
  
  # demonstrate reading of different files from different threads.
  # Releasing the Global Interpreter Lock (GIL) when calling the
  # netcdf C library for read operations speeds up the reads
  # when threads are used (issue 369).
  # Test script contributed by Ryan May of Unidata.
  
  # Make some files
  nfiles = 4
  fnames = []; datal = []
  for i in range(nfiles):
      fname = 'test%d.nc' % i
      fnames.append(fname)
      nc = Dataset(fname, 'w')
      data = np.random.randn(500, 500, 500)
      datal.append(data)
      nc.createDimension('x', 500)
      nc.createDimension('y', 500)
      nc.createDimension('z', 500)
      var = nc.createVariable('grid', 'f', ('x', 'y', 'z'))
      var[:] = data
      nc.close()
  
  # Queue them up
  items = Queue.Queue()
  for data,fname in zip(datal,fnames):
      items.put(fname)
  
  # Function for threads to use
  def get_data(serial=None):
      if serial is None: # if not called from a thread
          fname = items.get()
      else:
          fname = fnames[serial]
      nc = Dataset(fname, 'r')
      data2 = nc.variables['grid'][:]
      # make sure the data is correct
      #assert_array_almost_equal(data2,datal[int(fname[4])])
      nc.close()
      if serial is None:
         items.task_done()
  
  # Time it (no threading).
  start = time.time()
  for i in range(nfiles):
      get_data(serial=i)
  end = time.time()
  print 'no threads, time = ',end - start
  
  # with threading.
  start = time.time()
  for i in range(nfiles):
      threading.Thread(target=get_data).start()
  items.join()
  end = time.time()
  print 'with threading, time = ',end - start
  
subject: comment out check for correctness