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How to use prepare_test_case method in Testify

Best Python code snippet using Testify_python

test_countifs.py

Source:test_countifs.py

...210            (9, 2),211        ],212    },213]214def prepare_test_case(215    spark_context: SparkContext, test_case: Dict[str, List[Any]]216) -> Tuple[RDD, RDD, List[Any]]:217    data_rdd = spark_context.parallelize(218        enumerate(219            map(lambda p: p if isinstance(p, tuple) else (p,), test_case["data_points"])220        )221    )222    query_rdd = spark_context.parallelize(223        enumerate(224            map(225                lambda p: p if isinstance(p, tuple) else (p,),226                sorted(test_case["query_points"]),227            )228        )229    )230    return data_rdd, query_rdd, test_case["expected_result"]231@pytest.mark.parametrize("n_partitions", [1, 2, 4])232@pytest.mark.parametrize("test_case", TESTS_1D)233def test_algorithm_execution_1d(spark_context, n_partitions, test_case):234    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)235    countifs = Countifs(spark_context, n_partitions)236    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=1).collect()237    assert len(result) == len(expected_result)238    assert result == expected_result239TESTS_2D = [240    {241        "data_points": [(3, 6), (4, 2)],242        "query_points": [(0, 5), (7, 1)],243        "expected_result": [(0, 1), (1, 0)],244    },245    {246        "query_points": [(0, 5), (7, 1)],247        "data_points": [(3, 6), (4, 2)],248        "expected_result": [(0, 1), (1, 0)],249    },250    {251        "query_points": [(100, 100), (102, 102)],252        "data_points": [(103, 480), (1178, 101)],253        "expected_result": [(0, 2), (1, 1)],254    },255    {256        "query_points": [(100, 100), (102, 102), (104, 104), (106, 106)],257        "data_points": [(1178, 101), (103, 480), (105, 1771), (1243, 107)],258        "expected_result": [(0, 4), (1, 3), (2, 2), (3, 1)],259    },260    {261        "query_points": [(100, 100), (102, 102)],262        "data_points": [(103, 480), (105, 1771), (1178, 101), (1243, 107)],263        "expected_result": [(0, 4), (1, 3)],264    },265]266@pytest.mark.parametrize("n_partitions", [1, 2, 4])267@pytest.mark.parametrize("test_case", TESTS_2D)268def test_algorithm_execution_2d(spark_context, n_partitions, test_case):269    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)270    countifs = Countifs(spark_context, n_partitions)271    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=2).collect()272    assert len(result) == len(expected_result)273    assert result == expected_result274TESTS_3D = [275    {276        "query_points": [(100, 100, 100), (102, 102, 102)],277        "data_points": [278            (2137, 103, 480),279            (105, 2137, 1771),280            (1178, 101, 2137),281            (2137, 1243, 107),282        ],283        "expected_result": [(0, 4), (1, 3)],284    }285]286@pytest.mark.parametrize("n_partitions", [1, 2, 4])287@pytest.mark.parametrize("test_case", TESTS_3D)288def test_algorithm_execution_3d(spark_context, n_partitions, test_case):289    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)290    countifs = Countifs(spark_context, n_partitions)291    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=3).collect()292    assert len(result) == len(expected_result)293    assert result == expected_result294def random_test_case(n_data_points: int, n_query_points: int, n_dim: int):295    min_coord_value = 100296    max_coord_value = 100 + 100 * (n_query_points + n_data_points)297    def make_query_point(i):298        return tuple(min_coord_value + 2 * i for _ in range(n_dim))299    query_points = [make_query_point(i) for i in range(n_query_points)]300    data_points_per_query_point = n_data_points // n_query_points301    data_points_rest = n_data_points - n_query_points * data_points_per_query_point302    def make_data_point(min_val, max_val, global_max_val):303        """304        One of the coords will be between (min_val, max_val),305        rest of the coords will be between (min_val, global_max_val)306        """307        random_coord = random.randint(0, n_dim - 1)308        coords = [random.randint(min_val, global_max_val) for _ in range(n_dim)]309        coords[random_coord] = random.randint(min_val, max_val)310        return tuple(coords)311    # start with random data points which are smaller than all query points312    data_points = [313        make_data_point(0, min_coord_value, max_coord_value)314        for _ in range(data_points_rest)315    ]316    for i in range(n_query_points):317        # add data point in L-shape, with all dimensions > query point dimensions318        data_points_for_query = [319            make_data_point(320                min_coord_value + 2 * i + 1,321                min_coord_value + 2 * i + 1,322                max_coord_value,323            )324            for _ in range(data_points_per_query_point)325        ]326        data_points += data_points_for_query327    random.shuffle(data_points)328    expected_result = [329        (i, data_points_per_query_point * (n_query_points - i))330        for i in range(n_query_points)331    ]332    assert expected_result[-1] == (n_query_points - 1, data_points_per_query_point)333    assert (334        len(data_points) == n_data_points335    ), f"got: {len(data_points)}, expected: {n_data_points}"336    return {337        "data_points": data_points,338        "query_points": query_points,339        "expected_result": expected_result,340    }341LONG_N_PARTITIONS = [1, 2, 3, 4, 8, 16]342RANDOM_TESTS_1D = [343    random_test_case(10, 10, 1),344    random_test_case(1_000, 10, 1),345    random_test_case(1_000, 100, 1),346    random_test_case(1_000, 1_000, 1),347    # random_test_case(100_000, 10, 1),348    # random_test_case(100_000, 100, 1),349    # random_test_case(100_000, 1_000, 1),350    # random_test_case(100_000, 10_000, 1),351]352@pytest.mark.long353@pytest.mark.parametrize("n_partitions", LONG_N_PARTITIONS)354@pytest.mark.parametrize("test_case", RANDOM_TESTS_1D)355def test_algorithm_performance_1d(spark_context, n_partitions, test_case):356    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)357    countifs = Countifs(spark_context, n_partitions)358    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=1).collect()359    assert len(result) == len(expected_result)360    assert result == expected_result361RANDOM_TESTS_2D = [362    random_test_case(10, 10, 2),363    random_test_case(1_000, 10, 2),364    random_test_case(1_000, 100, 2),365    random_test_case(1_000, 1_000, 2),366    # random_test_case(100_000, 10, 2),367    # random_test_case(100_000, 100, 2),368    # random_test_case(100_000, 1_000, 2),369    # random_test_case(100_000, 10_000, 2),370]371@pytest.mark.long372@pytest.mark.parametrize("n_partitions", LONG_N_PARTITIONS)373@pytest.mark.parametrize("test_case", RANDOM_TESTS_2D)374def test_algorithm_performance_2d(spark_context, n_partitions, test_case):375    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)376    countifs = Countifs(spark_context, n_partitions)377    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=2).collect()378    assert len(result) == len(expected_result)379    assert result == expected_result380RANDOM_TESTS_3D = [381    random_test_case(10, 10, 3),382    random_test_case(1_000, 10, 3),383    random_test_case(1_000, 100, 3),384    random_test_case(1_000, 1_000, 3),385    # random_test_case(100_000, 10, 3),386    # random_test_case(100_000, 100, 3),387    # random_test_case(100_000, 1_000, 3),388    # random_test_case(100_000, 10_000, 3),389]390@pytest.mark.long391@pytest.mark.parametrize("n_partitions", LONG_N_PARTITIONS)392@pytest.mark.parametrize("test_case", RANDOM_TESTS_3D)393def test_algorithm_performance_3d(spark_context, n_partitions, test_case):394    data_rdd, query_rdd, expected_result = prepare_test_case(spark_context, test_case)395    countifs = Countifs(spark_context, n_partitions)396    result = countifs(data_rdd=data_rdd, query_rdd=query_rdd, n_dim=3).collect()397    assert len(result) == len(expected_result)...

test_runner_test.py

Source:test_runner_test.py

...7from testify import test_runner8from .test_runner_subdir.inheriting_class import InheritingClass9prepared = False10running = False11def prepare_test_case(options, test_case):12    global prepared13    prepared = True14def run_test_case(options, test_case, runnable):15    global running16    running = True17    try:18        return runnable()19    finally:20        running = False21def add_testcase_info(test_case, runner):22    test_case.__testattr__ = True23class TestTestRunnerGetTestMethodName(test_case.TestCase):24    def test_method_from_other_module_reports_class_module(self):25        ret = test_runner.TestRunner.get_test_method_name(...

add_validation_case.py

Source:add_validation_case.py

...12# get event data from production SKIP13skip = SKIP('https://skip.eatws.net',14            secret_id='skip-prod-readonly-access')15logging.basicConfig(stream=sys.stderr, level=logging.DEBUG)16def prepare_test_case(evid, inventory=None, waveforms=None):17    event: Optional[Event] = skip.get_event(evid)18    if not event:19        raise ValueError('%s not found in SKIP' % evid)20    eqinfo = dict(21        id=event.id,22        lat=event.latitude,23        lon=event.longitude,24        dep=event.depth_km,25        time=UTCDateTime(event.event_time),26    )27    datadir = abspath(DATA_DIR)28    result = runwphase(29        server='IRIS',30        eqinfo=eqinfo,31        save_waveforms=join(datadir, '%s.mseed' % evid),32        save_inventory=join(datadir, '%s.xml' % evid),33        inventory=inventory,34        waveforms=waveforms,35    )36    MT = result.MomentTensor37    case = dict(38        id=event.id,39        lat=event.latitude,40        lon=event.longitude,41        dep=event.depth_km,42        time=event.event_time,43        _expected_results={k: getattr(MT, k) for k in result_keys},44    )45    if result.QualityParams is not None:46        case["_expected_results"]["azimuthal_gap"] = result.QualityParams.azimuthal_gap47    print(json.dumps(case, indent=4))48    add_case(case)49    print("This test case has been added to validation_cases.json and test-datasets/.")50    print("To create a new release tarball: "51          "tar czvf ga-wphase-test-datasets.tar.gz test-datasets/ validation_cases.json")52if __name__ == '__main__':...

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