pipeline/tests/test_observations.py

from isora_hf.observations import (
    STATUS_EMPTY,
    STATUS_NOT_APPLICABLE,
    STATUS_NOT_AVAILABLE,
    STATUS_UNRECOGNIZED,
    STATUS_VALUE,
    clean_text,
    parse_value,
)


def test_numeric_values_are_parsed_to_float():
    parsed = parse_value(" 15.35949622 ")
    assert parsed.status == STATUS_VALUE
    assert parsed.numeric == 15.35949622
    assert parsed.text is None


def test_negative_and_exponent_numbers_are_numeric():
    assert parse_value("-3").numeric == -3.0
    assert parse_value("1e3").numeric == 1000.0


def test_nan_and_inf_strings_are_not_numeric():
    for raw in ("nan", "inf", "NaN", "Infinity"):
        parsed = parse_value(raw)
        assert parsed.numeric is None
        assert parsed.text == raw


def test_d_is_not_available_sentinel():
    parsed = parse_value("D")
    assert parsed.status == STATUS_NOT_AVAILABLE
    assert parsed.numeric is None and parsed.text is None


def test_p_is_flagged_as_unrecognized_but_kept():
    parsed = parse_value("P")
    assert parsed.status == STATUS_UNRECOGNIZED
    assert parsed.text == "P"


def test_not_applicable_variants():
    for raw in ("Not Applicable", "N/A", "not applicable "):
        assert parse_value(raw).status == STATUS_NOT_APPLICABLE


def test_empty_string_is_empty_status():
    assert parse_value("").status == STATUS_EMPTY
    assert parse_value(None).status == STATUS_EMPTY


def test_categorical_answers_keep_text_and_strip_html():
    parsed = parse_value("option a)<br/>")
    assert parsed.status == STATUS_VALUE
    assert parsed.text == "option a)"
    assert parsed.numeric is None


def test_clean_text_collapses_whitespace_and_entities():
    assert clean_text("  High &amp;   Medium\n priority ") == "High & Medium priority"


def test_repair_mojibake_handles_double_encoding_and_leaves_clean_text():
    from isora_hf.observations import repair_mojibake

    assert repair_mojibake("from ‘tax type’") == "from ‘tax type’"
    assert repair_mojibake("Türkiye") == "Türkiye"
    assert repair_mojibake("Türkiye ‘quoted’") == "Türkiye ‘quoted’"


def test_space_grouped_numbers_are_numeric():
    parsed = parse_value("163 310 020")
    assert parsed.status == STATUS_VALUE and parsed.numeric == 163310020.0


def test_observed_value_kind_inference():
    import pandas as pd

    from isora_hf.observations import observed_value_kind

    def kind(vals):
        parsed = [parse_value(v) for v in vals]
        return observed_value_kind(
            pd.Series([p.numeric for p in parsed], dtype="float64"),
            pd.Series([p.text for p in parsed], dtype="string"),
            pd.Series([p.status for p in parsed], dtype="string"),
        )

    assert kind(["1", "2.5", "D", ""]) == "numeric"
    assert kind(["Yes", "No", "yes", "D"]) == "binary"
    assert kind(["High", "Low", "Medium"]) == "categorical"
    assert kind(["1", "Yes"]) == "mixed"
    assert kind(["D", "", "Not Applicable"]) == "no_values"
    assert kind([f"free text {i}" for i in range(40)]) == "free_text"


def test_monetary_harmonization_by_generation():
    import pandas as pd

    from isora_hf.config import DATAFLOWS
    from isora_hf.observations import UNIT_LCU, UNIT_LCU_THOUSANDS, monetary_columns

    codes = pd.Series(["80040_3", "83690_206"])
    numeric = pd.Series([407383000.0, 120.0])
    unit, harmonized = monetary_columns(
        DATAFLOWS[1], codes, pd.Series([0, 0]), numeric, {"80040_3"}
    )
    assert unit.tolist()[0] == UNIT_LCU_THOUSANDS and pd.isna(unit.tolist()[1])
    assert harmonized.tolist()[0] == 407383000.0 * 1000 and pd.isna(harmonized.tolist()[1])

    unit, harmonized = monetary_columns(
        DATAFLOWS[2], codes, pd.Series([3, 0]), pd.Series([619373009000.0, 120.0]), set()
    )
    assert unit.tolist()[0] == UNIT_LCU and harmonized.tolist()[0] == 619373009000.0
    assert pd.isna(harmonized.tolist()[1])


def test_latest_derived_expenditure_aggregates_are_thousands():
    import pandas as pd

    from isora_hf.config import DATAFLOWS
    from isora_hf.observations import UNIT_LCU, UNIT_LCU_THOUSANDS, monetary_columns

    codes = pd.Series(["337_176", "80040_3", "337_001"])
    unit, harmonized = monetary_columns(
        DATAFLOWS[2], codes, pd.Series([0, 3, 0]), pd.Series([4398349.0, 6.27e11, 0.7]), set()
    )
    assert unit.tolist()[0] == UNIT_LCU_THOUSANDS and harmonized.tolist()[0] == 4398349000.0
    assert unit.tolist()[1] == UNIT_LCU and harmonized.tolist()[1] == 6.27e11
    assert pd.isna(unit.tolist()[2]) and pd.isna(harmonized.tolist()[2])