def test_correct_raw_data_2_inputs(self):
array1 = np.array([1, 2], dtype=np.dtype(np.int32))
array2 = np.array([3, 5], dtype=np.dtype(np.int32))
result = npw.cartesian_product_indices([array1, array2])
expected_array1_result = np.array([0, 0, 1, 1], dtype=np.int64)
expected_array2_result = np.array([0, 1, 0, 1], dtype=np.int64)
np.testing.assert_array_equal(expected_array1_result,
result[0].evaluate(verbose=False),
"First array result is incorrect")
np.testing.assert_array_equal(expected_array2_result,
result[1].evaluate(verbose=False),
"Second array result is incorrect")
def test_correct_lazy_data_input_second(self):
array1 = np.array([1, 2, 3], dtype=np.dtype(np.int32))
array2 = np.array([4, 5], dtype=np.dtype(np.int32))
array2 = npw.duplicate_elements_indices(array2, 2L)
result = npw.cartesian_product_indices([array1, array2])
expected_array1_result = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2], dtype=np.int64)
expected_array2_result = np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3], dtype=np.int64)
np.testing.assert_array_equal(expected_array1_result,
result[0].evaluate(verbose=False),
"First array result is incorrect")
np.testing.assert_array_equal(expected_array2_result,
result[1].evaluate(verbose=False),
"Second array result is incorrect")
def from_product(cls, levels, names):
""" Create MultiIndex when no labels are available
Parameters
----------
levels : list of np.ndarray or list of LazyResult
names : list of str
Returns
-------
MultiIndex
"""
labels = npw.cartesian_product_indices(levels, MultiIndex._cache_flag)
return cls(levels, labels, names)
def test_correct_lazy_data_input_both(self):
array1 = np.array([1, 2, 3], dtype=np.dtype(np.int32))
array1 = npw.duplicate_elements_indices(array1, 2L)
array2 = np.array([4, 5], dtype=np.dtype(np.int32))
array2 = npw.duplicate_elements_indices(array2, 2L)
result = npw.cartesian_product_indices([array1, array2])
# this was incorrect before; if one duplicate_elements([0, 0], 2L) it results in [0, 0, 0, 0]
# as values, however the indices are in fact [0, 0, 1, 1] which is what we requested
expected_array1_result = np.array([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3, 4, 4, 4, 4, 5, 5,
5, 5],
dtype=np.int64)
expected_array2_result = np.array([0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3, 0, 1, 2, 3],
dtype=np.int64)
np.testing.assert_array_equal(expected_array1_result,
result[0].evaluate(verbose=False),
"First array result is incorrect")
np.testing.assert_array_equal(expected_array2_result,
result[1].evaluate(verbose=False),
"Second array result is incorrect")
