old_contents: from skxray.core.correlation.correlation import (multi_tau_auto_corr,
                                                   intermediate_data)
  from skxray.core.accumulators.correlation import MultiTauCorrelation
  import numpy as np
  import pandas as pd
  # turn off auto wrapping of pandas dataframes
  pd.set_option('display.expand_frame_repr', False)
  
  
  def test_against_reference_implementation():
      num_levels = 6
      num_bufs = 4  # must be even
      xdim = 256
      ydim = 512
      stack_size = 100
      # img_stack = np.zeros((stack_size, xdim, ydim), dtype=int)
      img_stack = np.random.randint(1, 10, ((stack_size, xdim, ydim)))
      rois = np.zeros_like(img_stack[0])
      # make sure that the ROIs can be any integers greater than 1. They do not
      # have to start at 1 and be continuous
      rois[0:xdim//10, 0:ydim//10] = 5
      rois[xdim//10:xdim//5, ydim//10:ydim//5] = 3
      # run the correlation with the reference implementation
      gen = multi_tau_auto_corr(num_levels, num_bufs, rois, img_stack)
      res = list(gen)
      g2, lag_steps = res[-1]
      # run the correlation with the accumulator version
      mt = MultiTauCorrelation(num_levels, num_bufs, rois)
      res2 = []
      for img in img_stack:
          mt.process(img)
          res2.append(mt.get_current_state())
  
      equal = []
      for accum, full in zip(res2, res):
          for accum_item, full_item in zip(accum, full):
              equal.append(np.all(accum_item == full_item))
  
      equal = [np.all(accum_item == full_item)
               for accum, full in zip(res2, res)
               for accum_item, full_item in zip(accum, full)]
      df = pd.DataFrame(np.asarray(equal).reshape(len(res2), len(res2[0])),
                        columns=intermediate_data._fields)
  
      # compare the results
      assert np.all(mt.g2 == g2)
      assert np.all(mt.lag_steps == lag_steps)
  
      # reset the partial data correlator and check the results again
      mt.reset()
      for img in img_stack:
          mt.process(img)
  
      # compare the results
      assert np.all(mt.g2 == g2)
      assert np.all(mt.lag_steps == lag_steps)
  
      raise
  
new_contents: from skxray.core.correlation.correlation import (multi_tau_auto_corr,
                                                   intermediate_data)
  from skxray.core.accumulators.correlation import MultiTauCorrelation
  import numpy as np
  import pandas as pd
  # turn off auto wrapping of pandas dataframes
  pd.set_option('display.expand_frame_repr', False)
  
  
  def test_against_reference_implementation():
      num_levels = 6
      num_bufs = 4  # must be even
      xdim = 256
      ydim = 512
      stack_size = 100
      # img_stack = np.zeros((stack_size, xdim, ydim), dtype=int)
      img_stack = np.random.randint(1, 10, ((stack_size, xdim, ydim)))
      rois = np.zeros_like(img_stack[0])
      # make sure that the ROIs can be any integers greater than 1. They do not
      # have to start at 1 and be continuous
      rois[0:xdim//10, 0:ydim//10] = 5
      rois[xdim//10:xdim//5, ydim//10:ydim//5] = 3
      # run the correlation with the reference implementation
      gen = multi_tau_auto_corr(num_levels, num_bufs, rois, img_stack)
      res = list(gen)
      g2, lag_steps = res[-1]
      # run the correlation with the accumulator version
      mt = MultiTauCorrelation(num_levels, num_bufs, rois)
      res2 = []
      for img in img_stack:
          mt.process(img)
          res2.append(mt.get_current_state())
  
      equal = []
      for accum, full in zip(res2, res):
          for accum_item, full_item in zip(accum, full):
              equal.append(np.all(accum_item == full_item))
  
      equal = [np.all(accum_item == full_item)
               for accum, full in zip(res2, res)
               for accum_item, full_item in zip(accum, full)]
      df = pd.DataFrame(np.asarray(equal).reshape(len(res2), len(res2[0])),
                        columns=intermediate_data._fields)
  
      # compare the results
      assert np.all(mt.g2 == g2)
      assert np.all(mt.lag_steps == lag_steps)
  
      # reset the partial data correlator and check the results again
      mt.reset()
      for img in img_stack:
          mt.process(img)
  
      # compare the results
      assert np.all(mt.g2 == g2)
      assert np.all(mt.lag_steps == lag_steps)
  
subject: Remove 'raise' in the image_correlation test