text: import pytest
  import numpy as np
  from numpy.core import (
      array, arange, atleast_1d, atleast_2d, atleast_3d, block, vstack, hstack,
      newaxis, concatenate, stack
      )
  from numpy.core.shape_base import (_block_dispatcher, _block_setup,
                                     _block_concatenate, _block_slicing)
  from numpy.testing import (
      assert_, assert_raises, assert_array_equal, assert_equal,
      assert_raises_regex, assert_warns, IS_PYPY
      )
  
  
  class TestAtleast1d:
      def test_0D_array(self):
          a = array(1)
          b = array(2)
          res = [atleast_1d(a), atleast_1d(b)]
          desired = [array([1]), array([2])]
          assert_array_equal(res, desired)
  
      def test_1D_array(self):
          a = array([1, 2])
          b = array([2, 3])
          res = [atleast_1d(a), atleast_1d(b)]
          desired = [array([1, 2]), array([2, 3])]
          assert_array_equal(res, desired)
  
      def test_2D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          res = [atleast_1d(a), atleast_1d(b)]
          desired = [a, b]
          assert_array_equal(res, desired)
  
      def test_3D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          a = array([a, a])
          b = array([b, b])
          res = [atleast_1d(a), atleast_1d(b)]
          desired = [a, b]
          assert_array_equal(res, desired)
  
      def test_r1array(self):
          """ Test to make sure equivalent Travis O's r1array function
          """
          assert_(atleast_1d(3).shape == (1,))
          assert_(atleast_1d(3j).shape == (1,))
          assert_(atleast_1d(3.0).shape == (1,))
          assert_(atleast_1d([[2, 3], [4, 5]]).shape == (2, 2))
  
  
  class TestAtleast2d:
      def test_0D_array(self):
          a = array(1)
          b = array(2)
          res = [atleast_2d(a), atleast_2d(b)]
          desired = [array([[1]]), array([[2]])]
          assert_array_equal(res, desired)
  
      def test_1D_array(self):
          a = array([1, 2])
          b = array([2, 3])
          res = [atleast_2d(a), atleast_2d(b)]
          desired = [array([[1, 2]]), array([[2, 3]])]
          assert_array_equal(res, desired)
  
      def test_2D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          res = [atleast_2d(a), atleast_2d(b)]
          desired = [a, b]
          assert_array_equal(res, desired)
  
      def test_3D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          a = array([a, a])
          b = array([b, b])
          res = [atleast_2d(a), atleast_2d(b)]
          desired = [a, b]
          assert_array_equal(res, desired)
  
      def test_r2array(self):
          """ Test to make sure equivalent Travis O's r2array function
          """
          assert_(atleast_2d(3).shape == (1, 1))
          assert_(atleast_2d([3j, 1]).shape == (1, 2))
          assert_(atleast_2d([[[3, 1], [4, 5]], [[3, 5], [1, 2]]]).shape == (2, 2, 2))
  
  
  class TestAtleast3d:
      def test_0D_array(self):
          a = array(1)
          b = array(2)
          res = [atleast_3d(a), atleast_3d(b)]
          desired = [array([[[1]]]), array([[[2]]])]
          assert_array_equal(res, desired)
  
      def test_1D_array(self):
          a = array([1, 2])
          b = array([2, 3])
          res = [atleast_3d(a), atleast_3d(b)]
          desired = [array([[[1], [2]]]), array([[[2], [3]]])]
          assert_array_equal(res, desired)
  
      def test_2D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          res = [atleast_3d(a), atleast_3d(b)]
          desired = [a[:,:, newaxis], b[:,:, newaxis]]
          assert_array_equal(res, desired)
  
      def test_3D_array(self):
          a = array([[1, 2], [1, 2]])
          b = array([[2, 3], [2, 3]])
          a = array([a, a])
          b = array([b, b])
          res = [atleast_3d(a), atleast_3d(b)]
          desired = [a, b]
          assert_array_equal(res, desired)
  
  
  class TestHstack:
      def test_non_iterable(self):
          assert_raises(TypeError, hstack, 1)
  
      def test_empty_input(self):
          assert_raises(ValueError, hstack, ())
  
      def test_0D_array(self):
          a = array(1)
          b = array(2)
          res = hstack([a, b])
          desired = array([1, 2])
          assert_array_equal(res, desired)
  
      def test_1D_array(self):
          a = array([1])
          b = array([2])
          res = hstack([a, b])
          desired = array([1, 2])
          assert_array_equal(res, desired)
  
      def test_2D_array(self):
          a = array([[1], [2]])
          b = array([[1], [2]])
          res = hstack([a, b])
          desired = array([[1, 1], [2, 2]])
          assert_array_equal(res, desired)
  
      def test_generator(self):
          with assert_warns(FutureWarning):
              hstack((np.arange(3) for _ in range(2)))
          with assert_warns(FutureWarning):
              hstack(map(lambda x: x, np.ones((3, 2))))
  
  
  class TestVstack:
      def test_non_iterable(self):
          assert_raises(TypeError, vstack, 1)
  
      def test_empty_input(self):
          assert_raises(ValueError, vstack, ())
  
      def test_0D_array(self):
          a = array(1)
          b = array(2)
          res = vstack([a, b])
          desired = array([[1], [2]])
          assert_array_equal(res, desired)
  
      def test_1D_array(self):
          a = array([1])
          b = array([2])
          res = vstack([a, b])
          desired = array([[1], [2]])
          assert_array_equal(res, desired)
  
      def test_2D_array(self):
          a = array([[1], [2]])
          b = array([[1], [2]])
          res = vstack([a, b])
          desired = array([[1], [2], [1], [2]])
          assert_array_equal(res, desired)
  
      def test_2D_array2(self):
          a = array([1, 2])
          b = array([1, 2])
          res = vstack([a, b])
          desired = array([[1, 2], [1, 2]])
          assert_array_equal(res, desired)
  
      def test_generator(self):
          with assert_warns(FutureWarning):
              vstack((np.arange(3) for _ in range(2)))
  
  
  class TestConcatenate:
      def test_returns_copy(self):
          a = np.eye(3)
          b = np.concatenate([a])
          b[0, 0] = 2
          assert b[0, 0] != a[0, 0]
  
      def test_exceptions(self):
          # test axis must be in bounds
          for ndim in [1, 2, 3]:
              a = np.ones((1,)*ndim)
              np.concatenate((a, a), axis=0)  # OK
              assert_raises(np.AxisError, np.concatenate, (a, a), axis=ndim)
              assert_raises(np.AxisError, np.concatenate, (a, a), axis=-(ndim + 1))
  
          # Scalars cannot be concatenated
          assert_raises(ValueError, concatenate, (0,))
          assert_raises(ValueError, concatenate, (np.array(0),))
  
          # dimensionality must match
          assert_raises_regex(
              ValueError,
              r"all the input arrays must have same number of dimensions, but "
              r"the array at index 0 has 1 dimension\(s\) and the array at "
              r"index 1 has 2 dimension\(s\)",
              np.concatenate, (np.zeros(1), np.zeros((1, 1))))
  
          # test shapes must match except for concatenation axis
          a = np.ones((1, 2, 3))
          b = np.ones((2, 2, 3))
          axis = list(range(3))
          for i in range(3):
              np.concatenate((a, b), axis=axis[0])  # OK
              assert_raises_regex(
                  ValueError,
                  "all the input array dimensions for the concatenation axis "
                  "must match exactly, but along dimension {}, the array at "
                  "index 0 has size 1 and the array at index 1 has size 2"
                  .format(i),
                  np.concatenate, (a, b), axis=axis[1])
              assert_raises(ValueError, np.concatenate, (a, b), axis=axis[2])
              a = np.moveaxis(a, -1, 0)
              b = np.moveaxis(b, -1, 0)
              axis.append(axis.pop(0))
  
          # No arrays to concatenate raises ValueError
          assert_raises(ValueError, concatenate, ())
  
      def test_concatenate_axis_None(self):
          a = np.arange(4, dtype=np.float64).reshape((2, 2))
          b = list(range(3))
          c = ['x']
          r = np.concatenate((a, a), axis=None)
          assert_equal(r.dtype, a.dtype)
          assert_equal(r.ndim, 1)
          r = np.concatenate((a, b), axis=None)
          assert_equal(r.size, a.size + len(b))
          assert_equal(r.dtype, a.dtype)
          r = np.concatenate((a, b, c), axis=None)
          d = array(['0.0', '1.0', '2.0', '3.0',
                     '0', '1', '2', 'x'])
          assert_array_equal(r, d)
  
          out = np.zeros(a.size + len(b))
          r = np.concatenate((a, b), axis=None)
          rout = np.concatenate((a, b), axis=None, out=out)
          assert_(out is rout)
          assert_equal(r, rout)
  
      def test_large_concatenate_axis_None(self):
          # When no axis is given, concatenate uses flattened versions.
          # This also had a bug with many arrays (see gh-5979).
          x = np.arange(1, 100)
          r = np.concatenate(x, None)
          assert_array_equal(x, r)
  
          # This should probably be deprecated:
          r = np.concatenate(x, 100)  # axis is >= MAXDIMS
          assert_array_equal(x, r)
  
      def test_concatenate(self):
          # Test concatenate function
          # One sequence returns unmodified (but as array)
          r4 = list(range(4))
          assert_array_equal(concatenate((r4,)), r4)
          # Any sequence
          assert_array_equal(concatenate((tuple(r4),)), r4)
          assert_array_equal(concatenate((array(r4),)), r4)
          # 1D default concatenation
          r3 = list(range(3))
          assert_array_equal(concatenate((r4, r3)), r4 + r3)
          # Mixed sequence types
          assert_array_equal(concatenate((tuple(r4), r3)), r4 + r3)
          assert_array_equal(concatenate((array(r4), r3)), r4 + r3)
          # Explicit axis specification
          assert_array_equal(concatenate((r4, r3), 0), r4 + r3)
          # Including negative
          assert_array_equal(concatenate((r4, r3), -1), r4 + r3)
          # 2D
          a23 = array([[10, 11, 12], [13, 14, 15]])
          a13 = array([[0, 1, 2]])
          res = array([[10, 11, 12], [13, 14, 15], [0, 1, 2]])
          assert_array_equal(concatenate((a23, a13)), res)
          assert_array_equal(concatenate((a23, a13), 0), res)
          assert_array_equal(concatenate((a23.T, a13.T), 1), res.T)
          assert_array_equal(concatenate((a23.T, a13.T), -1), res.T)
          # Arrays much match shape
          assert_raises(ValueError, concatenate, (a23.T, a13.T), 0)
          # 3D
          res = arange(2 * 3 * 7).reshape((2, 3, 7))
          a0 = res[..., :4]
          a1 = res[..., 4:6]
          a2 = res[..., 6:]
          assert_array_equal(concatenate((a0, a1, a2), 2), res)
          assert_array_equal(concatenate((a0, a1, a2), -1), res)
          assert_array_equal(concatenate((a0.T, a1.T, a2.T), 0), res.T)
  
          out = res.copy()
          rout = concatenate((a0, a1, a2), 2, out=out)
          assert_(out is rout)
          assert_equal(res, rout)
  
      @pytest.mark.skipif(IS_PYPY, reason="PYPY handles sq_concat, nb_add differently than cpython")
      def test_operator_concat(self):
          import operator
          a = array([1, 2])
          b = array([3, 4])
          n = [1,2]
          res = array([1, 2, 3, 4])
          assert_raises(TypeError, operator.concat, a, b)
          assert_raises(TypeError, operator.concat, a, n)
          assert_raises(TypeError, operator.concat, n, a)
          assert_raises(TypeError, operator.concat, a, 1)
          assert_raises(TypeError, operator.concat, 1, a)
  
      def test_bad_out_shape(self):
          a = array([1, 2])
          b = array([3, 4])
  
          assert_raises(ValueError, concatenate, (a, b), out=np.empty(5))
          assert_raises(ValueError, concatenate, (a, b), out=np.empty((4,1)))
          assert_raises(ValueError, concatenate, (a, b), out=np.empty((1,4)))
          concatenate((a, b), out=np.empty(4))
  
      @pytest.mark.parametrize("axis", [None, 0])
      @pytest.mark.parametrize("out_dtype", ["c8", "f4", "f8", ">f8", "i8", "S4"])
      @pytest.mark.parametrize("casting",
              ['no', 'equiv', 'safe', 'same_kind', 'unsafe'])
      def test_out_and_dtype(self, axis, out_dtype, casting):
          # Compare usage of `out=out` with `dtype=out.dtype`
          out = np.empty(4, dtype=out_dtype)
          to_concat = (array([1.1, 2.2]), array([3.3, 4.4]))
  
          if not np.can_cast(to_concat[0], out_dtype, casting=casting):
              with assert_raises(TypeError):
                  concatenate(to_concat, out=out, axis=axis, casting=casting)
              with assert_raises(TypeError):
                  concatenate(to_concat, dtype=out.dtype,
                              axis=axis, casting=casting)
          else:
              res_out = concatenate(to_concat, out=out,
                                    axis=axis, casting=casting)
              res_dtype = concatenate(to_concat, dtype=out.dtype,
                                      axis=axis, casting=casting)
              assert res_out is out
              assert_array_equal(out, res_dtype)
              assert res_dtype.dtype == out_dtype
  
          with assert_raises(TypeError):
              concatenate(to_concat, out=out, dtype=out_dtype, axis=axis)
  
      @pytest.mark.parametrize("axis", [None, 0])
      @pytest.mark.parametrize("string_dt", ["S", "U", "S0", "U0"])
      @pytest.mark.parametrize("arrs",
              [([0.],), ([0.], [1]), ([0], ["string"], [1.])])
      def test_dtype_with_promotion(self, arrs, string_dt, axis):
          # Note that U0 and S0 should be deprecated eventually and changed to
          # actually give the empty string result (together with `np.array`)
          res = np.concatenate(arrs, axis=axis, dtype=string_dt, casting="unsafe")
          assert res.dtype == np.promote_types("d", string_dt)
  
      @pytest.mark.parametrize("axis", [None, 0])
      def test_string_dtype_does_not_inspect(self, axis):
          # The error here currently depends on NPY_USE_NEW_CASTINGIMPL as
          # the new version rejects using the "default string length" of 64.
          # The new behaviour is better, `np.array()` and `arr.astype()` would
          # have to be used instead. (currently only raises due to unsafe cast)
          with pytest.raises((ValueError, TypeError)):
              np.concatenate(([None], [1]), dtype="S", axis=axis)
          with pytest.raises((ValueError, TypeError)):
              np.concatenate(([None], [1]), dtype="U", axis=axis)
  
      @pytest.mark.parametrize("axis", [None, 0])
      def test_subarray_error(self, axis):
          with pytest.raises(TypeError, match=".*subarray dtype"):
              np.concatenate(([1], [1]), dtype="(2,)i", axis=axis)
  
  
  def test_stack():
      # non-iterable input
      assert_raises(TypeError, stack, 1)
  
      # 0d input
      for input_ in [(1, 2, 3),
                     [np.int32(1), np.int32(2), np.int32(3)],
                     [np.array(1), np.array(2), np.array(3)]]:
          assert_array_equal(stack(input_), [1, 2, 3])
      # 1d input examples
      a = np.array([1, 2, 3])
      b = np.array([4, 5, 6])
      r1 = array([[1, 2, 3], [4, 5, 6]])
      assert_array_equal(np.stack((a, b)), r1)
      assert_array_equal(np.stack((a, b), axis=1), r1.T)
      # all input types
      assert_array_equal(np.stack(list([a, b])), r1)
      assert_array_equal(np.stack(array([a, b])), r1)
      # all shapes for 1d input
      arrays = [np.random.randn(3) for _ in range(10)]
      axes = [0, 1, -1, -2]
      expected_shapes = [(10, 3), (3, 10), (3, 10), (10, 3)]
      for axis, expected_shape in zip(axes, expected_shapes):
          assert_equal(np.stack(arrays, axis).shape, expected_shape)
      assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=2)
      assert_raises_regex(np.AxisError, 'out of bounds', stack, arrays, axis=-3)
      # all shapes for 2d input
      arrays = [np.random.randn(3, 4) for _ in range(10)]
      axes = [0, 1, 2, -1, -2, -3]
      expected_shapes = [(10, 3, 4), (3, 10, 4), (3, 4, 10),
                         (3, 4, 10), (3, 10, 4), (10, 3, 4)]
      for axis, expected_shape in zip(axes, expected_shapes):
          assert_equal(np.stack(arrays, axis).shape, expected_shape)
      # empty arrays
      assert_(stack([[], [], []]).shape == (3, 0))
      assert_(stack([[], [], []], axis=1).shape == (0, 3))
      # out
      out = np.zeros_like(r1)
      np.stack((a, b), out=out)
      assert_array_equal(out, r1)
      # edge cases
      assert_raises_regex(ValueError, 'need at least one array', stack, [])
      assert_raises_regex(ValueError, 'must have the same shape',
                          stack, [1, np.arange(3)])
      assert_raises_regex(ValueError, 'must have the same shape',
                          stack, [np.arange(3), 1])
      assert_raises_regex(ValueError, 'must have the same shape',
                          stack, [np.arange(3), 1], axis=1)
      assert_raises_regex(ValueError, 'must have the same shape',
                          stack, [np.zeros((3, 3)), np.zeros(3)], axis=1)
      assert_raises_regex(ValueError, 'must have the same shape',
                          stack, [np.arange(2), np.arange(3)])
      # generator is deprecated
      with assert_warns(FutureWarning):
          result = stack((x for x in range(3)))
      assert_array_equal(result, np.array([0, 1, 2]))
  
  
  class TestBlock:
      @pytest.fixture(params=['block', 'force_concatenate', 'force_slicing'])
      def block(self, request):
          # blocking small arrays and large arrays go through different paths.
          # the algorithm is triggered depending on the number of element
          # copies required.
          # We define a test fixture that forces most tests to go through
          # both code paths.
          # Ultimately, this should be removed if a single algorithm is found
          # to be faster for both small and large arrays.
          def _block_force_concatenate(arrays):
              arrays, list_ndim, result_ndim, _ = _block_setup(arrays)
              return _block_concatenate(arrays, list_ndim, result_ndim)
  
          def _block_force_slicing(arrays):
              arrays, list_ndim, result_ndim, _ = _block_setup(arrays)
              return _block_slicing(arrays, list_ndim, result_ndim)
  
          if request.param == 'force_concatenate':
              return _block_force_concatenate
          elif request.param == 'force_slicing':
              return _block_force_slicing
          elif request.param == 'block':
              return block
          else:
              raise ValueError('Unknown blocking request. There is a typo in the tests.')
  
      def test_returns_copy(self, block):
          a = np.eye(3)
          b = block(a)
          b[0, 0] = 2
          assert b[0, 0] != a[0, 0]
  
      def test_block_total_size_estimate(self, block):
          _, _, _, total_size = _block_setup([1])
          assert total_size == 1
  
          _, _, _, total_size = _block_setup([[1]])
          assert total_size == 1
  
          _, _, _, total_size = _block_setup([[1, 1]])
          assert total_size == 2
  
          _, _, _, total_size = _block_setup([[1], [1]])
          assert total_size == 2
  
          _, _, _, total_size = _block_setup([[1, 2], [3, 4]])
          assert total_size == 4
  
      def test_block_simple_row_wise(self, block):
          a_2d = np.ones((2, 2))
          b_2d = 2 * a_2d
          desired = np.array([[1, 1, 2, 2],
                              [1, 1, 2, 2]])
          result = block([a_2d, b_2d])
          assert_equal(desired, result)
  
      def test_block_simple_column_wise(self, block):
          a_2d = np.ones((2, 2))
          b_2d = 2 * a_2d
          expected = np.array([[1, 1],
                               [1, 1],
                               [2, 2],
                               [2, 2]])
          result = block([[a_2d], [b_2d]])
          assert_equal(expected, result)
  
      def test_block_with_1d_arrays_row_wise(self, block):
          # # # 1-D vectors are treated as row arrays
          a = np.array([1, 2, 3])
          b = np.array([2, 3, 4])
          expected = np.array([1, 2, 3, 2, 3, 4])
          result = block([a, b])
          assert_equal(expected, result)
  
      def test_block_with_1d_arrays_multiple_rows(self, block):
          a = np.array([1, 2, 3])
          b = np.array([2, 3, 4])
          expected = np.array([[1, 2, 3, 2, 3, 4],
                               [1, 2, 3, 2, 3, 4]])
          result = block([[a, b], [a, b]])
          assert_equal(expected, result)
  
      def test_block_with_1d_arrays_column_wise(self, block):
          # # # 1-D vectors are treated as row arrays
          a_1d = np.array([1, 2, 3])
          b_1d = np.array([2, 3, 4])
          expected = np.array([[1, 2, 3],
                               [2, 3, 4]])
          result = block([[a_1d], [b_1d]])
          assert_equal(expected, result)
  
      def test_block_mixed_1d_and_2d(self, block):
          a_2d = np.ones((2, 2))
          b_1d = np.array([2, 2])
          result = block([[a_2d], [b_1d]])
          expected = np.array([[1, 1],
                               [1, 1],
                               [2, 2]])
          assert_equal(expected, result)
  
      def test_block_complicated(self, block):
          # a bit more complicated
          one_2d = np.array([[1, 1, 1]])
          two_2d = np.array([[2, 2, 2]])
          three_2d = np.array([[3, 3, 3, 3, 3, 3]])
          four_1d = np.array([4, 4, 4, 4, 4, 4])
          five_0d = np.array(5)
          six_1d = np.array([6, 6, 6, 6, 6])
          zero_2d = np.zeros((2, 6))
  
          expected = np.array([[1, 1, 1, 2, 2, 2],
                               [3, 3, 3, 3, 3, 3],
                               [4, 4, 4, 4, 4, 4],
                               [5, 6, 6, 6, 6, 6],
                               [0, 0, 0, 0, 0, 0],
                               [0, 0, 0, 0, 0, 0]])
  
          result = block([[one_2d, two_2d],
                          [three_2d],
                          [four_1d],
                          [five_0d, six_1d],
                          [zero_2d]])
          assert_equal(result, expected)
  
      def test_nested(self, block):
          one = np.array([1, 1, 1])
          two = np.array([[2, 2, 2], [2, 2, 2], [2, 2, 2]])
          three = np.array([3, 3, 3])
          four = np.array([4, 4, 4])
          five = np.array(5)
          six = np.array([6, 6, 6, 6, 6])
          zero = np.zeros((2, 6))
  
          result = block([
              [
                  block([
                     [one],
                     [three],
                     [four]
                  ]),
                  two
              ],
              [five, six],
              [zero]
          ])
          expected = np.array([[1, 1, 1, 2, 2, 2],
                               [3, 3, 3, 2, 2, 2],
                               [4, 4, 4, 2, 2, 2],
                               [5, 6, 6, 6, 6, 6],
                               [0, 0, 0, 0, 0, 0],
                               [0, 0, 0, 0, 0, 0]])
  
          assert_equal(result, expected)
  
      def test_3d(self, block):
          a000 = np.ones((2, 2, 2), int) * 1
  
          a100 = np.ones((3, 2, 2), int) * 2
          a010 = np.ones((2, 3, 2), int) * 3
          a001 = np.ones((2, 2, 3), int) * 4
  
          a011 = np.ones((2, 3, 3), int) * 5
          a101 = np.ones((3, 2, 3), int) * 6
          a110 = np.ones((3, 3, 2), int) * 7
  
          a111 = np.ones((3, 3, 3), int) * 8
  
          result = block([
              [
                  [a000, a001],
                  [a010, a011],
              ],
              [
                  [a100, a101],
                  [a110, a111],
              ]
          ])
          expected = array([[[1, 1, 4, 4, 4],
                             [1, 1, 4, 4, 4],
                             [3, 3, 5, 5, 5],
                             [3, 3, 5, 5, 5],
                             [3, 3, 5, 5, 5]],
  
                            [[1, 1, 4, 4, 4],
                             [1, 1, 4, 4, 4],
                             [3, 3, 5, 5, 5],
                             [3, 3, 5, 5, 5],
                             [3, 3, 5, 5, 5]],
  
                            [[2, 2, 6, 6, 6],
                             [2, 2, 6, 6, 6],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8]],
  
                            [[2, 2, 6, 6, 6],
                             [2, 2, 6, 6, 6],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8]],
  
                            [[2, 2, 6, 6, 6],
                             [2, 2, 6, 6, 6],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8],
                             [7, 7, 8, 8, 8]]])
  
          assert_array_equal(result, expected)
  
      def test_block_with_mismatched_shape(self, block):
          a = np.array([0, 0])
          b = np.eye(2)
          assert_raises(ValueError, block, [a, b])
          assert_raises(ValueError, block, [b, a])
  
          to_block = [[np.ones((2,3)), np.ones((2,2))],
                      [np.ones((2,2)), np.ones((2,2))]]
          assert_raises(ValueError, block, to_block)
      def test_no_lists(self, block):
          assert_equal(block(1),         np.array(1))
          assert_equal(block(np.eye(3)), np.eye(3))
  
      def test_invalid_nesting(self, block):
          msg = 'depths are mismatched'
          assert_raises_regex(ValueError, msg, block, [1, [2]])
          assert_raises_regex(ValueError, msg, block, [1, []])
          assert_raises_regex(ValueError, msg, block, [[1], 2])
          assert_raises_regex(ValueError, msg, block, [[], 2])
          assert_raises_regex(ValueError, msg, block, [
              [[1], [2]],
              [[3, 4]],
              [5]  # missing brackets
          ])
  
      def test_empty_lists(self, block):
          assert_raises_regex(ValueError, 'empty', block, [])
          assert_raises_regex(ValueError, 'empty', block, [[]])
          assert_raises_regex(ValueError, 'empty', block, [[1], []])
  
      def test_tuple(self, block):
          assert_raises_regex(TypeError, 'tuple', block, ([1, 2], [3, 4]))
          assert_raises_regex(TypeError, 'tuple', block, [(1, 2), (3, 4)])
  
      def test_different_ndims(self, block):
          a = 1.
          b = 2 * np.ones((1, 2))
          c = 3 * np.ones((1, 1, 3))
  
          result = block([a, b, c])
          expected = np.array([[[1., 2., 2., 3., 3., 3.]]])
  
          assert_equal(result, expected)
  
      def test_different_ndims_depths(self, block):
          a = 1.
          b = 2 * np.ones((1, 2))
          c = 3 * np.ones((1, 2, 3))
  
          result = block([[a, b], [c]])
          expected = np.array([[[1., 2., 2.],
                                [3., 3., 3.],
                                [3., 3., 3.]]])
  
          assert_equal(result, expected)
  
      def test_block_memory_order(self, block):
          # 3D
          arr_c = np.zeros((3,)*3, order='C')
          arr_f = np.zeros((3,)*3, order='F')
  
          b_c = [[[arr_c, arr_c],
                  [arr_c, arr_c]],
                 [[arr_c, arr_c],
                  [arr_c, arr_c]]]
  
          b_f = [[[arr_f, arr_f],
                  [arr_f, arr_f]],
                 [[arr_f, arr_f],
                  [arr_f, arr_f]]]
  
          assert block(b_c).flags['C_CONTIGUOUS']
          assert block(b_f).flags['F_CONTIGUOUS']
  
          arr_c = np.zeros((3, 3), order='C')
          arr_f = np.zeros((3, 3), order='F')
          # 2D
          b_c = [[arr_c, arr_c],
                 [arr_c, arr_c]]
  
          b_f = [[arr_f, arr_f],
                 [arr_f, arr_f]]
  
          assert block(b_c).flags['C_CONTIGUOUS']
          assert block(b_f).flags['F_CONTIGUOUS']
  
  
  def test_block_dispatcher():
      class ArrayLike:
          pass
      a = ArrayLike()
      b = ArrayLike()
      c = ArrayLike()
      assert_equal(list(_block_dispatcher(a)), [a])
      assert_equal(list(_block_dispatcher([a])), [a])
      assert_equal(list(_block_dispatcher([a, b])), [a, b])
      assert_equal(list(_block_dispatcher([[a], [b, [c]]])), [a, b, c])
      # don't recurse into non-lists
      assert_equal(list(_block_dispatcher((a, b))), [(a, b)])
  