#-------------------------------------------------------------------------------------
#
#-------------------------------------------------------------------------------------
should "work with stat_lists" do
  # Creates a DoubleStatList of capacity 10
  @list = DoubleStatList.new(10)
  @list.add(2.25)
  @list.add(5.0)
  assert_equal(2.25, @list[0])
  assert_equal(5.0, @list[1])
  assert_equal(3.625, @list.mean)
  # Search for element in the list
  assert_equal(1, @list.binary_search(5))
  assert_equal(1, @list.index_of(5.0))
  @list.add(3.0)
  @list.add(5.0)
  
  assert_equal(3, @list.last_index_of(5.0))
  list2 = @list.copy
  # changing the value of @list
  @list[0] = 10
  assert_equal(10, @list[0])
  # list2 is unchanged
  assert_equal(2.25, list2[0])
  @list.shuffle
  @list.print
  # mean is now wrong... we've added/changed elements in @list, but we still get the
  assert_equal(3.625, @list.mean)
  # to make it right we need to reset_statistics.  Whenever an element on the 
  # list is changed it is required to call reset_statistics
  @list.reset_statistics
  assert_equal(5.75, @list.mean)
end
#-------------------------------------------------------------------------------------
#
#-------------------------------------------------------------------------------------
should "be memory efficient when possible" do
  # read file VALE3.  This file has a header that we need to discard.  VALE3 
  # contains quote values from brazilian company VALE as obtained from Yahoo finance
  # (quote vale3.SA)
  vale3 = MDArray.double("#{$COLT_TEST_DIR}/VALE3_short.csv", true)
  # calling reset_statistics on vale3, creates a stat_list for vale3
  vale3.reset_statistics
  # get vale3 stat_list.  This should not copy any data... let's check it
  list = vale3.stat_list
  
  # let's change a value in list
  list[0] = 10
  assert_equal(10, list[0])
  assert_equal(10, vale3[0, 0])
  # Getting the open value
  open = vale3.slice(1, 1)
  
  # open and vale3 should use the same backing store
  open[0] = 5
  assert_equal(5, open[0])
  assert_equal(5, vale3[0,1])
  # now... doing reset_statistics on open will not use the same backing store
  # as open is not contigous in memory and copying is necessary.  This is the
  # cost integrating NetCDF and Parallel Colt.  It might actually be a good 
  # idea in some cases to make a copy and operate on contigous memory than using
  # indexing to move over the array.
  open.reset_statistics
  open_list = open.stat_list
  open_list[0] = 1000
  assert_equal(1000, open_list[0])
  assert_equal(5, open[0])
  
end