How to use recreate method in tox

Best Python code snippet using tox_python

test_se5.py

Source:test_se5.py

1import sys2import os3import math4# Handle the fact that the test code may not5# be in the same directory as the solution code6sys.path.insert(0, os.getcwd())7import se58import numpy as np9MODULE = "se5"10# # #11#12# HELPER FUNCTIONS13#14# # #15def pretty_print_repr(x):16    """17    A version of repr with some special casing.18    """19    if isinstance(x, np.ndarray):20        return "np." + repr(x)21    return repr(x)22def gen_recreate_msg(module, function, *params, **kwparams):23        24    params_str = ", ".join([pretty_print_repr(p) for p in params])25    if len(kwparams) > 0:26        params_str += ", ".join(["{} = {}".format(k, repr(v)) for k, v in kwparams.items()])27    recreate_msg = "To recreate this test in ipython3 run:\n"28    recreate_msg += "  {}.{}({})".format(module, function, params_str)29    return recreate_msg30def check_none(actual, recreate_msg=None):31    msg = "The function returned None."32    msg += " Did you forget to replace the placeholder value we provide?"33    if recreate_msg is not None:34        msg += "\n" + recreate_msg35    assert actual is not None, msg36def check_type(actual, expected, recreate_msg=None):37    actual_type = type(actual)38    expected_type = type(expected)39    msg = "The function returned a value of the wrong type.\n"40    msg += "  Expected return type: {}.\n".format(expected_type.__name__)41    msg += "  Actual return type: {}.".format(actual_type.__name__)42    if recreate_msg is not None:43        msg += "\n" + recreate_msg44    assert isinstance(actual, expected_type), msg45def check_dtype(array, expected, recreate_msg=None):46    actual_dtype = array.dtype47    expected_dtype = expected48    msg = "The function returned an array of the wrong dtype.\n"49    msg += "  Expected return dtype: {}.\n".format(expected_dtype)50    msg += "  Actual return dtype: {}.".format(actual_dtype)51    if recreate_msg is not None:52        msg += "\n" + recreate_msg53    assert actual_dtype == expected_dtype, msg54def check_array_equal(actual, expected, recreate_msg):55    msg = "The function returned the wrong array"56    msg += " Expected array: {}\n".format(expected)57    msg += " Actual returned array: {}\n".format(actual)58    if recreate_msg is not None:59        msg += "\n" + recreate_msg60    61    np.testing.assert_allclose(actual, expected,62                               err_msg = msg, verbose=False)63    64    65    # # #66#67# TEST HELPERS68#69# # #70def check_is_ndarray(actual, recreate_msg=None):71    check_type(actual, np.zeros(1), recreate_msg)72    73def test_compute_matching_values():74    x = np.array([1, 2, 3, 4])75    y = np.array([1, 5, 3, 2])76    recreate_msg = gen_recreate_msg(MODULE, 'compute_matching',77                                    x, y)78    79    result = se5.compute_matching(x, y)80    check_none(result, recreate_msg)81    check_is_ndarray(result, recreate_msg)82    check_dtype(result, bool, recreate_msg)83        84    85    check_array_equal(result, np.array([True, False, True, False]),86                      recreate_msg)87def test_compute_matching_indices():88    x = np.array([1, 2, 3, 4])89    y = np.array([1, 5, 3, 2])90    recreate_msg = gen_recreate_msg(MODULE, 'compute_matching_indices',91                                    x, y)92    93    result = se5.compute_matching_indices(x, y)94    check_none(result, recreate_msg)95    check_is_ndarray(result, recreate_msg)96    check_dtype(result, int, recreate_msg)97        98    99    check_array_equal(result, np.array([0, 2]),100                      recreate_msg)101    102def test_powers():103    for p in [1, 2, 4]:104        for N in [1, 2, 5, 10]:105            recreate_msg = gen_recreate_msg(MODULE, 'powers',106                                            N, p)107            108    109            result = se5.powers(N, p)110            check_none(result, recreate_msg)111            check_is_ndarray(result, recreate_msg)112            check_array_equal(result,113                              np.array([p**i for i in range(N)]),114                              recreate_msg)115def test_clip_values():116    x = np.linspace(0, 2, 10)117    x_orig = x.copy()118    ### Check for modification of input array119    recreate_msg = gen_recreate_msg(MODULE, 'clip_values',120                                    min_val=1, max_val=1.8)121    122    result = se5.clip_values(x, min_val=1, max_val=1.8)123    124    check_none(result, recreate_msg)125    check_is_ndarray(result, recreate_msg)126    assert np.allclose(x, x_orig), \127        "\n Input array was modified.\n\n" + recreate_msg128    129    ### Check what happens with no specification130    recreate_msg = gen_recreate_msg(MODULE, 'clip_values')131    result = se5.clip_values(x)132    133    check_none(result, recreate_msg)134    check_is_ndarray(result, recreate_msg)135    assert np.max(result) == np.max(x), \136    "\n The max value of the array was modified when max_val=None\n\n" \137        + recreate_msg138    139    assert np.min(result) == np.min(x), \140        "\n The min value of the array was modified when min_val=None\n\n" \141        + recreate_msg142    143    144    ### Check minimum value145    recreate_msg = gen_recreate_msg(MODULE, 'clip_values', min_val =1)146    result = se5.clip_values(x, min_val=1)147    148    check_none(result, recreate_msg)149    check_is_ndarray(result, recreate_msg)150    assert np.min(result) == 1.0,\151        "\n The minimum value of the array is not 1.0\n\n" \152        + recreate_msg153    154    ### Check maximum value155    recreate_msg = gen_recreate_msg(MODULE, 'clip_values', max_val =1)156    result = se5.clip_values(x, max_val=1)157    158    check_none(result, recreate_msg)159    check_is_ndarray(result, recreate_msg)160    assert np.max(result) == 1.0, \161        "\n The maximum value of the array is not 1.0\n\n" \162        + recreate_msg163    164    ### Check Both165    recreate_msg = gen_recreate_msg(MODULE, 'clip_values', min_val =1.0, max_val=1.5)166    result = se5.clip_values(x, min_val=1.0, max_val=1.5)167    168    check_none(result, recreate_msg)169    check_is_ndarray(result, recreate_msg)170    assert np.max(result) == 1.5, \171    "\n The maximum value of the array is not 1.5\n\n"\172        + recreate_msg173    assert np.min(result) == 1.0, \174    "\n The minimum value of the array is not 1.0\n\n"\175        + recreate_msg176        177def test_find_closest_value():178    def manual_closest_value(x):179        closest_delta = 1e9180        181        closest_idx = None182        closest_val = None183        184        for i, x_item in enumerate(x):185            delta = abs(x_item - np.mean(x))186            if delta < closest_delta:187                closest_delta = delta188                closest_idx = i189                closest_val = x_item190        assert closest_idx is not None191        192        return closest_idx, closest_val193        194    195    x = np.array([1.0, 1.1, 1.2, 1.3, 1.4, 1.5])196        197    recreate_msg = gen_recreate_msg(MODULE, 'find_closest_value', 198                                    x)199    result = se5.find_closest_value(x)200    check_none(result, recreate_msg)201    expected_closest_val = manual_closest_value(x)202    assert expected_closest_val == result, \203        "\n function returned {} as closest val ".format(result) \204        + "but we expected {}\n\n".format(expected_closest_val) \205        + recreate_msg206def test_select_row_col():207    x = np.arange(20).reshape(4, 5)208    def custom_get_cols(x, cols):209        out = np.stack([x[:, r] for r in cols], -1)210        return out211    212    def custom_get_rows(x, rows):213        out = np.stack([x[r] for r in rows])214        return out215    216    def custom_get_rows_cols(x, rows, cols):217        row_out = np.stack([x[r] for r in rows])218        out = np.stack([row_out[:, r] for r in cols], -1)219        return out220    221        222    for tgt_cols in [[0], [1, 2, 3], [3, 2, 1], [2, 1, 3, 4, 0]]:223        recreate_msg = gen_recreate_msg(MODULE, 'select_row_col', x, None, tgt_cols)224        225        result = se5.select_row_col(x, None, tgt_cols)226    227        check_none(result, recreate_msg)228        check_is_ndarray(result, recreate_msg)229        expected_shape = (4, len(tgt_cols))230        assert result.shape == expected_shape, \231            "The shape of the returned array was {}, but".format(result.shape) \232            + " we expected {}\n\n".format(expected_shape) + recreate_msg233        expected_value = custom_get_cols(x, tgt_cols)234        check_array_equal(result, expected_value, 235                          recreate_msg)236        237    for tgt_rows in [[0], [1, 2, 3], [3, 2, 1], [2, 1, 3, 0]]:238        recreate_msg = gen_recreate_msg(MODULE, 'select_row_col', x, tgt_rows, None)239        240        result = se5.select_row_col(x, tgt_rows, None)241    242        check_none(result, recreate_msg)243        check_is_ndarray(result, recreate_msg)244        expected_shape = (len(tgt_rows), 5)245        assert result.shape == expected_shape, \246            "The shape of the returned array was {}, but".format(result.shape) \247            + " we expected {}\n\n".format(expected_shape) + recreate_msg248        expected_value = custom_get_rows(x, tgt_rows)249        check_array_equal(result, expected_value, 250                          recreate_msg)251    252    for tgt_rows, tgt_cols in [([0],[0]), ([1, 2, 3],[1, 3]), ([3, 1],[0,2])]:253        recreate_msg = gen_recreate_msg(MODULE, 'select_row_col', x, tgt_rows, tgt_cols)254        255        result = se5.select_row_col(x, tgt_rows, tgt_cols)256    257        check_none(result, recreate_msg)258        check_is_ndarray(result, recreate_msg)259        expected_shape = (len(tgt_rows), len(tgt_cols))260        assert result.shape == expected_shape, \261            "The shape of the returned array was {}, but".format(result.shape) \262            + " we expected {}\n\n".format(expected_shape) + recreate_msg263        expected_value = custom_get_rows_cols(x, tgt_rows, tgt_cols)264        check_array_equal(result, expected_value, 265                          recreate_msg)266    267    268    for tgt_rows, tgt_cols in [(None, None)]:269        recreate_msg = gen_recreate_msg(MODULE, 'select_row_col', x, None, None)270        271        result = se5.select_row_col(x, None, None)272    273        check_none(result, recreate_msg)274        check_is_ndarray(result, recreate_msg)275        expected_shape = (x.shape[0], x.shape[1])276        assert result.shape == expected_shape, \277            "The shape of the returned array was {}, but".format(result.shape) \278            + " we expected {}\n\n".format(expected_shape) + recreate_msg279        check_array_equal(result, x, recreate_msg)280        ...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.