superset/tests/unit_tests/utils/test_core.py

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import os
from typing import Any, Optional

import pandas as pd
import pytest

from superset.utils.core import (
    cast_to_boolean,
    DateColumn,
    is_test,
    normalize_dttm_col,
    parse_boolean_string,
    QueryObjectFilterClause,
    remove_extra_adhoc_filters,
)

ADHOC_FILTER: QueryObjectFilterClause = {
    "col": "foo",
    "op": "==",
    "val": "bar",
}

EXTRA_FILTER: QueryObjectFilterClause = {
    "col": "foo",
    "op": "==",
    "val": "bar",
    "isExtra": True,
}


@pytest.mark.parametrize(
    "original,expected",
    [
        ({"foo": "bar"}, {"foo": "bar"}),
        (
            {"foo": "bar", "adhoc_filters": [ADHOC_FILTER]},
            {"foo": "bar", "adhoc_filters": [ADHOC_FILTER]},
        ),
        (
            {"foo": "bar", "adhoc_filters": [EXTRA_FILTER]},
            {"foo": "bar", "adhoc_filters": []},
        ),
        (
            {
                "foo": "bar",
                "adhoc_filters": [ADHOC_FILTER, EXTRA_FILTER],
            },
            {"foo": "bar", "adhoc_filters": [ADHOC_FILTER]},
        ),
        (
            {
                "foo": "bar",
                "adhoc_filters_b": [ADHOC_FILTER, EXTRA_FILTER],
            },
            {"foo": "bar", "adhoc_filters_b": [ADHOC_FILTER]},
        ),
        (
            {
                "foo": "bar",
                "custom_adhoc_filters": [
                    ADHOC_FILTER,
                    EXTRA_FILTER,
                ],
            },
            {
                "foo": "bar",
                "custom_adhoc_filters": [
                    ADHOC_FILTER,
                    EXTRA_FILTER,
                ],
            },
        ),
    ],
)
def test_remove_extra_adhoc_filters(
    original: dict[str, Any], expected: dict[str, Any]
) -> None:
    remove_extra_adhoc_filters(original)
    assert expected == original


def test_is_test():
    orig_value = os.getenv("SUPERSET_TESTENV")

    os.environ["SUPERSET_TESTENV"] = "true"
    assert is_test()
    os.environ["SUPERSET_TESTENV"] = "false"
    assert not is_test()
    os.environ["SUPERSET_TESTENV"] = ""
    assert not is_test()

    if orig_value is not None:
        os.environ["SUPERSET_TESTENV"] = orig_value


@pytest.mark.parametrize(
    "test_input,expected",
    [
        ("y", True),
        ("Y", True),
        ("yes", True),
        ("True", True),
        ("t", True),
        ("true", True),
        ("On", True),
        ("on", True),
        ("1", True),
        ("n", False),
        ("N", False),
        ("no", False),
        ("False", False),
        ("f", False),
        ("false", False),
        ("Off", False),
        ("off", False),
        ("0", False),
        ("foo", False),
        (None, False),
    ],
)
def test_parse_boolean_string(test_input: Optional[str], expected: bool):
    assert parse_boolean_string(test_input) == expected


def test_int_values():
    assert cast_to_boolean(1) is True
    assert cast_to_boolean(0) is False
    assert cast_to_boolean(-1) is True
    assert cast_to_boolean(42) is True
    assert cast_to_boolean(0) is False


def test_float_values():
    assert cast_to_boolean(0.5) is True
    assert cast_to_boolean(3.14) is True
    assert cast_to_boolean(-2.71) is True
    assert cast_to_boolean(0.0) is False


def test_string_values():
    assert cast_to_boolean("true") is True
    assert cast_to_boolean("TruE") is True
    assert cast_to_boolean("false") is False
    assert cast_to_boolean("FaLsE") is False
    assert cast_to_boolean("") is False


def test_none_value():
    assert cast_to_boolean(None) is None


def test_boolean_values():
    assert cast_to_boolean(True) is True
    assert cast_to_boolean(False) is False


def test_other_values():
    assert cast_to_boolean([]) is False
    assert cast_to_boolean({}) is False
    assert cast_to_boolean(object()) is False


def test_normalize_dttm_col() -> None:
    """
    Tests for the ``normalize_dttm_col`` function.

    In particular, this covers a regression when Pandas was upgraded from 1.5.3 to
    2.0.3 and the behavior of ``pd.to_datetime`` changed.
    """
    df = pd.DataFrame({"__time": ["2017-07-01T00:00:00.000Z"]})
    assert (
        df.to_markdown()
        == """
|    | __time                   |
|---:|:-------------------------|
|  0 | 2017-07-01T00:00:00.000Z |
    """.strip()
    )

    # in 1.5.3 this would return a datetime64[ns] dtype, but in 2.0.3 we had to
    # add ``exact=False`` since there is a leftover after parsing the format
    dttm_cols = (DateColumn("__time", "%Y-%m-%d"),)

    # the function modifies the dataframe in place
    normalize_dttm_col(df, dttm_cols)

    assert df["__time"].astype(str).tolist() == ["2017-07-01"]