pipeline/src/isora_hf/observations.py

"""Turn the raw SDMX-CSV observation files into one clean, typed, long-format table."""

from __future__ import annotations

import html
import logging
import re
from dataclasses import dataclass

import pandas as pd

from isora_hf import config
from isora_hf.config import Dataflow

log = logging.getLogger(__name__)

NUMERIC_RE = re.compile(r"^[+-]?(\d+\.?\d*|\.\d+)([eE][+-]?\d+)?$")
SPACE_GROUPED_RE = re.compile(r"^-?\d{1,3}( \d{3})+$")
HTML_TAG_RE = re.compile(r"<[^>]+>")
WS_RE = re.compile(r"\s+")
MOJIBAKE_RE = re.compile(r"Ã|â€|Â")

STATUS_VALUE = "value"
STATUS_NOT_AVAILABLE = "not_available"
STATUS_NOT_APPLICABLE = "not_applicable"
STATUS_EMPTY = "empty"
STATUS_UNRECOGNIZED = "unrecognized_code"

UNIT_LCU = "local currency units"
UNIT_LCU_THOUSANDS = "thousands of local currency"
THOUSAND = 1000.0

KIND_NUMERIC = "numeric"
KIND_BINARY = "binary"
KIND_CATEGORICAL = "categorical"
KIND_TEXT = "free_text"
KIND_MIXED = "mixed"
KIND_NO_VALUES = "no_values"
MAX_CATEGORIES = 25


@dataclass(frozen=True)
class ParsedValue:
    numeric: float | None
    text: str | None
    status: str


def repair_mojibake(text: str, max_rounds: int = 3) -> str:
    """Undo UTF-8 text that was decoded as cp1252 one or more times (e.g. '‘' -> '‘')."""
    out = text
    for _ in range(max_rounds):
        if not MOJIBAKE_RE.search(out):
            break
        try:
            candidate = out.encode("cp1252").decode("utf-8")
        except (UnicodeEncodeError, UnicodeDecodeError):
            break
        if candidate == out:
            break
        out = candidate
    return out


def clean_text(raw: str) -> str:
    """Strip HTML tags and entities that leak from the survey UI, repair encoding glitches,
    collapse whitespace."""
    no_tags = HTML_TAG_RE.sub(" ", html.unescape(raw))
    return WS_RE.sub(" ", repair_mojibake(no_tags)).strip()


def parse_value(raw: str | None) -> ParsedValue:
    """Split the mixed-type OBSERVATION string into numeric / text / status."""
    stripped = (raw or "").strip()
    if not stripped:
        return ParsedValue(None, None, STATUS_EMPTY)
    if stripped in config.NOT_AVAILABLE_CODES:
        return ParsedValue(None, None, STATUS_NOT_AVAILABLE)
    if stripped in config.UNRECOGNIZED_CODES:
        return ParsedValue(None, stripped, STATUS_UNRECOGNIZED)
    if SPACE_GROUPED_RE.match(stripped):
        stripped = stripped.replace(" ", "")
    if NUMERIC_RE.match(stripped):
        return ParsedValue(float(stripped), None, STATUS_VALUE)
    text = clean_text(stripped)
    if text.lower() in config.NOT_APPLICABLE_STRINGS:
        return ParsedValue(None, text, STATUS_NOT_APPLICABLE)
    return ParsedValue(None, text, STATUS_VALUE)


def observed_value_kind(numeric: pd.Series, text: pd.Series, status: pd.Series) -> str:
    """Infer what kind of answers an indicator actually holds from its parsed values."""
    valued = status == STATUS_VALUE
    n_num = int((valued & numeric.notna()).sum())
    n_txt = int((valued & text.notna()).sum())
    if n_num == 0 and n_txt == 0:
        return KIND_NO_VALUES
    if n_txt == 0:
        return KIND_NUMERIC
    if n_num > 0:
        return KIND_MIXED
    answers = set(text[valued & text.notna()].str.lower())
    if answers <= {"yes", "no"}:
        return KIND_BINARY
    return KIND_CATEGORICAL if len(answers) <= MAX_CATEGORIES else KIND_TEXT


def read_raw(flow_id: str, version: str) -> pd.DataFrame:
    path = config.RAW_DATA_DIR / f"{flow_id}__{version}.csv"
    frame = pd.read_csv(path, dtype=str, keep_default_na=False, encoding="utf-8")
    # Rows without a TIME_PERIOD are dataset/series-level attribute rows, not observations.
    return frame[frame["TIME_PERIOD"] != ""].copy()


def _survey_round(year: int) -> str:
    try:
        return config.FISCAL_YEAR_TO_ROUND[year]
    except KeyError as exc:
        raise ValueError(f"fiscal year {year} has no known ISORA round") from exc


def monetary_columns(
    flow: Dataflow,
    indicator_code: pd.Series,
    unit_multiplier: pd.Series,
    numeric: pd.Series,
    monetary_codes: set[str],
) -> tuple[pd.Series, pd.Series]:
    """Harmonize money amounts to base local-currency units.

    ISORA 2016/2018 published monetary answers in thousands (as asked on the form) with SCALE=0.
    The consolidated FY2018+ dataflow publishes the same questions already multiplied out to base
    units and marks them with SCALE=3 (verified against GDP and revenue magnitudes)."""
    if flow.generation == "ISORA 2020+":
        is_units = unit_multiplier == 3
        is_thousands = indicator_code.isin(config.LATEST_THOUSANDS_CODES)
        unit = (
            pd.Series(pd.NA, index=indicator_code.index, dtype="string")
            .mask(is_units, UNIT_LCU)
            .mask(is_thousands, UNIT_LCU_THOUSANDS)
        )
        harmonized = numeric.where(is_units).mask(is_thousands, numeric * THOUSAND)
    else:
        is_money = indicator_code.isin(monetary_codes)
        unit = pd.Series(pd.NA, index=indicator_code.index, dtype="string").mask(
            is_money, UNIT_LCU_THOUSANDS
        )
        harmonized = (numeric * THOUSAND).where(is_money)
    return unit, harmonized


def clean_observations(
    flow: Dataflow,
    indicators: pd.DataFrame,
    numeric_to_alpha3: dict[str, str],
    jurisdiction_names: dict[str, str],
    monetary_codes: set[str],
) -> pd.DataFrame:
    raw = read_raw(flow.id, flow.version)
    log.info("%s: %d observation rows", flow.id, len(raw))
    if not (raw["PUBLIC_DATA"].str.lower() == "true").all():
        raise ValueError(f"{flow.id}: found rows not flagged PUBLIC_DATA=true")

    geo = raw[flow.geo_dimension]
    if geo.str.fullmatch(r"\d+").all():
        unmapped = sorted(set(geo) - set(numeric_to_alpha3))
        if unmapped:
            raise ValueError(f"{flow.id}: numeric jurisdiction codes without alpha-3: {unmapped}")
        jurisdiction_code = geo.map(numeric_to_alpha3)
    else:
        jurisdiction_code = geo

    parsed = [parse_value(v) for v in raw["OBSERVATION"]]
    labels = indicators.set_index("indicator_code")["label"]
    unknown = sorted(set(raw["INDICATOR"]) - set(labels.index))
    if unknown:
        raise ValueError(f"{flow.id}: indicator codes missing from codelist: {unknown[:10]}")

    fiscal_year = raw["TIME_PERIOD"].astype(int)
    numeric = pd.Series([p.numeric for p in parsed], index=raw.index, dtype="float64")
    text = pd.Series([p.text for p in parsed], index=raw.index, dtype="string")
    status = pd.Series([p.status for p in parsed], index=raw.index, dtype="string")
    unit_multiplier = raw["SCALE"].replace("", "0").astype(int)
    unit, harmonized = monetary_columns(
        flow, raw["INDICATOR"], unit_multiplier, numeric, monetary_codes
    )
    kinds = (
        pd.DataFrame({"i": raw["INDICATOR"], "n": numeric, "t": text, "s": status})
        .groupby("i")
        .apply(lambda g: observed_value_kind(g["n"], g["t"], g["s"]), include_groups=False)
    )

    out = pd.DataFrame(
        {
            "jurisdiction_code": jurisdiction_code.values,
            "jurisdiction_name": jurisdiction_code.map(jurisdiction_names).values,
            "fiscal_year": fiscal_year.astype("int16").values,
            "survey_round": [_survey_round(y) for y in fiscal_year],
            "questionnaire_generation": flow.generation,
            "indicator_code": raw["INDICATOR"].values,
            "indicator_label": raw["INDICATOR"].map(labels).values,
            "indicator_value_kind": raw["INDICATOR"].map(kinds).values,
            "value_raw": raw["OBSERVATION"].values,
            "value_numeric": numeric.values,
            "value_text": text.values,
            "value_status": status.values,
            "unit_multiplier": unit_multiplier.astype("int8").values,
            "monetary_unit": unit.values,
            "value_local_currency_units": harmonized.values,
            "form_status": raw[flow.form_status_attr].replace("", None).values,
            "footnote": raw["FOOTNOTE"].map(lambda s: clean_text(s) or None).values,
            "source_dataflow": flow.id,
            "source_dataflow_version": flow.version,
        }
    )
    missing_names = out.loc[out["jurisdiction_name"].isna(), "jurisdiction_code"].unique()
    if len(missing_names):
        raise ValueError(f"{flow.id}: jurisdictions without a name: {sorted(missing_names)}")
    dup = out.duplicated(["jurisdiction_code", "indicator_code", "fiscal_year"]).sum()
    if dup:
        raise ValueError(f"{flow.id}: {dup} duplicate (jurisdiction, indicator, year) keys")
    return out.sort_values(["jurisdiction_code", "indicator_code", "fiscal_year"]).reset_index(
        drop=True
    )


OBSERVATION_COLUMNS = [
    "jurisdiction_code",
    "jurisdiction_name",
    "fiscal_year",
    "survey_round",
    "questionnaire_generation",
    "indicator_code",
    "indicator_label",
    "indicator_value_kind",
    "value_raw",
    "value_numeric",
    "value_text",
    "value_status",
    "unit_multiplier",
    "monetary_unit",
    "value_local_currency_units",
    "form_status",
    "footnote",
    "source_dataflow",
    "source_dataflow_version",
]