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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        ...

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