old_contents: #!/usr/bin/env python
  
  import numpy as np
  from numpy import median as np_median
  
  from scipy import percentile as sp_percentile
  
  from .._quantile import _quantile, _median
  
  from numpy.testing import (assert_array_equal,
                             assert_array_almost_equal)
  
  
  NUMERIC_TYPES = sum([np.sctypes[t]
                       for t in ('int', 'uint', 'float', 'complex')],
                      [])
  
  
  def test_median():
      for dtype in NUMERIC_TYPES:
          for shape in ((10,), (10, 11), (10, 11, 12)):
              X = (100 * (np.random.random(shape) - .5)).astype(dtype)
              for a in range(X.ndim):
                  assert_array_equal(_median(X, axis=a).squeeze(),
                                     np_median(X.astype(np.float64), axis=a))
  
  
  def test_quantile():
      for dtype in NUMERIC_TYPES:
          for shape in ((10,), (10, 11), (10, 11, 12)):
              X = (100 * (np.random.random(shape) - .5)).astype(dtype)
              for a in range(X.ndim):
                  assert_array_almost_equal(
                      _quantile(X, .75, axis=a, interp=True).squeeze(),
                      sp_percentile(X, 75, axis=a))
  
  
  if __name__ == "__main__":
      import nose
      nose.run(argv=['', __file__])
  
new_contents: #!/usr/bin/env python
  
  import numpy as np
  from numpy import median as np_median
  
  from scipy import percentile as sp_percentile
  
  from .._quantile import _quantile, _median
  
  from numpy.testing import (assert_array_equal,
                             assert_array_almost_equal)
  
  
  NUMERIC_TYPES = sum([np.sctypes[t]
                       for t in ('int', 'uint', 'float', 'complex')],
                      [])
  
  
  def another_percentile(arr, pct, axis):
      return np.apply_along_axis(sp_percentile, axis, arr.astype(float), pct)
  
  
  def test_median():
      for dtype in NUMERIC_TYPES:
          for shape in ((10,), (10, 11), (10, 11, 12)):
              X = (100 * (np.random.random(shape) - .5)).astype(dtype)
              for a in range(X.ndim):
                  assert_array_equal(_median(X, axis=a).squeeze(),
                                     np_median(X.astype(np.float64), axis=a))
  
  
  def test_quantile():
      for dtype in NUMERIC_TYPES:
          for shape in ((10,), (10, 11), (10, 11, 12)):
              X = (100 * (np.random.random(shape) - .5)).astype(dtype)
              for a in range(X.ndim):
                  assert_array_almost_equal(
                      _quantile(X, .75, axis=a, interp=True).squeeze(),
                      sp_percentile(X, 75, axis=a))
  
  
  if __name__ == "__main__":
      import nose
      nose.run(argv=['', __file__])
  
subject: fix badly considered import by some loser*