old_contents: 
  # -*- coding: utf-8 -*-
  
  
  from __future__ import unicode_literals
  from __future__ import division
  from __future__ import absolute_import
  from __future__ import print_function
  
  
  import numpy as np
  from numpy.testing import assert_array_almost_equal
  
  from sktracker import data
  
  from sktracker.tracker.cost_function.brownian import BrownianLinkCostFunction
  from sktracker.tracker.cost_function.brownian import BrownianGapCloseCostFunction
  
  
  def test_brownian_link():
  
      trajs = data.brownian_trajs_df()
      times_stamp = trajs.index.get_level_values('t_stamp').unique()
  
      t0 = times_stamp[0]
      t1 = times_stamp[1]
      pos0 = trajs.ix[t0]
      pos1 = trajs.ix[t1]
  
      cost_func = BrownianLinkCostFunction(parameters={'max_speed': 5.})
      cost_func.context['pos_in'] = pos0
      cost_func.context['pos_out'] = pos1
  
      cost_func.get_block()
      true_block = [[np.nan, np.nan, 1.551135510361437, np.nan, np.nan],
                    [np.nan, np.nan, np.nan, np.nan, 4.092519418118163],
                    [np.nan, np.nan, np.nan, 0.6004135878472862, np.nan],
                    [np.nan, 2.1081358741648697, np.nan, np.nan, np.nan],
                    [4.033127275182495, np.nan, np.nan, np.nan, np.nan]]
  
      assert_array_almost_equal(cost_func.mat, true_block)
  
  
  def test_brownian_gap_close():
  
      cost_func = BrownianGapCloseCostFunction(parameters={'max_speed': 5.})
  
new_contents: # -*- coding: utf-8 -*-
  
  from __future__ import unicode_literals
  from __future__ import division
  from __future__ import absolute_import
  from __future__ import print_function
  
  import numpy as np
  from numpy.testing import assert_array_almost_equal
  
  from sktracker import data
  
  from sktracker.tracker.cost_function.brownian import BrownianLinkCostFunction
  from sktracker.tracker.cost_function.brownian import BrownianGapCloseCostFunction
  from sktracker.trajectories import Trajectories
  
  
  def test_brownian_link():
  
      trajs = data.brownian_trajs_df()
      times_stamp = trajs.index.get_level_values('t_stamp').unique()
  
      t0 = times_stamp[0]
      t1 = times_stamp[1]
      pos0 = trajs.ix[t0]
      pos1 = trajs.ix[t1]
  
      cost_func = BrownianLinkCostFunction(parameters={'max_speed': 5.})
      cost_func.context['pos_in'] = pos0
      cost_func.context['pos_out'] = pos1
  
      cost_func.get_block()
      true_block = [[np.nan, np.nan, 1.551135510361437, np.nan, np.nan],
                    [np.nan, np.nan, np.nan, np.nan, 4.092519418118163],
                    [np.nan, np.nan, np.nan, 0.6004135878472862, np.nan],
                    [np.nan, 2.1081358741648697, np.nan, np.nan, np.nan],
                    [4.033127275182495, np.nan, np.nan, np.nan, np.nan]]
  
      assert_array_almost_equal(cost_func.mat, true_block)
  
  
  def test_brownian_gap_close():
  
      trajs = Trajectories(data.with_gaps_df())
      max_speed = 10.
  
      cost_func = BrownianGapCloseCostFunction(parameters={'max_speed': max_speed})
  
      in_idxs = [(3, 0), (3, 0), (5, 1), (13, 2), (13, 2), (16, 3)]
      out_idxs = [(5, 3), (7, 4), (7, 4), (15, 5), (18, 6), (18, 6)]
  
      cost_func.context['trajs'] = trajs
      cost_func.context['idxs_in'] = in_idxs
      cost_func.context['idxs_out'] = out_idxs
  
      cost_func.get_block()
      m = cost_func.mat
  
      m_true = [[np.nan, np.nan, np.nan, 2.0553543815820543, 45.74823093541038, np.nan, np.nan],
                [np.nan, np.nan, np.nan, np.nan, 1.1530875267623917, np.nan, np.nan],
                [np.nan, np.nan, np.nan, np.nan, np.nan, 0.8254178191605767, 23.84513283483594],
                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, 4.916599888362159],
                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan],
                [np.nan, np.nan, np.nan, np.nan, np.nan, np.nan, np.nan]]
  
      assert_array_almost_equal(m, m_true)
  
subject: Add real test for BrownianGapCloseCostFunction