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How to use test_is_t method in hypothesis

Best Python code snippet using hypothesis

test_from_dtype.py

Source:test_from_dtype.py

...48    assert nps.from_dtype(dtype).has_reusable_values49@pytest.mark.parametrize("t", STANDARD_TYPES)50def test_produces_instances(t):51    @given(nps.from_dtype(t))52    def test_is_t(x):53        assert isinstance(x, t.type)54        assert x.dtype.kind == t.kind55    test_is_t()56@settings(max_examples=100)57@given(nps.nested_dtypes(max_itemsize=400), st.data())58def test_infer_strategy_from_dtype(dtype, data):59    # Given a dtype60    assert isinstance(dtype, np.dtype)61    # We can infer a strategy62    strat = nps.from_dtype(dtype)63    assert isinstance(strat, SearchStrategy)64    # And use it to fill an array of that dtype65    data.draw(nps.arrays(dtype, 10, elements=strat))66@given(st.data())67def test_can_cast_for_scalars(data):68    # Note: this only passes with castable datatypes, certain dtype69    # combinations will result in an error if numpy is not able to cast them....

test_gen_data.py

Source:test_gen_data.py

...34]))35@pytest.mark.parametrize(u't', STANDARD_TYPES)36def test_produces_instances(t):37    @given(from_dtype(t))38    def test_is_t(x):39        assert isinstance(x, t.type)40        assert x.dtype.kind == t.kind41    test_is_t()42@given(arrays(float, ()))43def test_empty_dimensions_are_scalars(x):44    assert isinstance(x, np.dtype(float).type)45@given(arrays(u'uint32', (5, 5)))46def test_generates_unsigned_ints(x):47    assert (x >= 0).all()48@given(arrays(int, (1,)))49def test_assert_fits_in_machine_size(x):50    pass51def test_generates_and_minimizes():52    x = find(arrays(float, (2, 2)), lambda t: True)53    assert (x == np.zeros(shape=(2, 2), dtype=float)).all()54def test_can_minimize_large_arrays():55    x = find(arrays(u'uint32', 500), lambda t: t.any())...

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