"""analyze_comparison — compare a metric across two groups (KM-608).

An analytical "family" tool: in ONE call it aggregates a value for two groups
of a dimension (e.g. region "A" vs "B", channel "online" vs "store") and
reports the gap between them — absolute difference, percent difference, and
direction. group_a is treated as the baseline. Answers questions like
"how does revenue in region A compare to region B?".

STATUS: compute layer only — the function takes an already-materialized
DataFrame. The wrapper layer (fetching data from the catalog via source_id,
the ToolOutput envelope, ToolSpec registration) is added once the Planner
seam (KM-418) is settled. Keeping compute separate from data-fetching makes
this function easy to unit-test in isolation and stable when wrapped.
"""

from __future__ import annotations

import pandas as pd

from src.tools.analytics.descriptive import ColumnNotFoundError

# How to aggregate the value within each group before comparing.
SUPPORTED_AGGS = ("sum", "mean", "count", "min", "max", "median")


class UnsupportedAggregationError(ValueError):
    """The requested aggregation is not supported (maps to error_code UNSUPPORTED_AGG)."""


class GroupNotFoundError(ValueError):
    """A requested group value does not occur in the dimension column (maps to GROUP_NOT_FOUND)."""




# Prompt-style description read by the Planner to decide WHEN to pick this tool.
# Final destination is ToolSpec.description once the wrapper layer is built.
DESCRIPTION = """\
Summary: Head-to-head comparison of one aggregated metric between TWO specific \
groups of a dimension (group_a is the baseline). Reports each group's value, \
the absolute and percent difference, and which side is higher.

USE WHEN the question pits two named groups against each other. Trigger words: \
"vs"/"versus", "compare" (bandingkan), "A or B", "difference between" \
(selisih/beda antara), "higher/lower than".

SETTING GROUPS: group_a is the BASELINE (the reference). The "comparison" field \
reads as "group_b is {higher/lower/equal} than group_a", and diff = value_b - \
value_a. Put the reference/older/expected side in group_a. E.g. "is this year \
higher than last year" -> group_a=last year, group_b=this year.

DON'T USE WHEN:
  - it aggregates across many groups at once -> analyze_aggregate
  - it splits a single total into shares -> analyze_contribution
  - it tracks change over time -> analyze_trend

Example questions:
  - "compare revenue between Jakarta and Surabaya"
  - "is the average order value higher for members or non-members?"
  - "difference in churn between plan A and plan B"
  - "male vs female average spend"
"""


def analyze_comparison(
    df: pd.DataFrame,
    dimension: str,
    value_column: str,
    group_a: object,
    group_b: object,
    agg: str = "sum",
) -> dict[str, object]:
    """Compare one aggregated metric between two groups of a dimension.

    Args:
        df: already-materialized data (in the real system the wrapper fetches
            this from a source_id).
        dimension: the categorical column whose values define the two groups.
        value_column: numeric column to aggregate for each group.
        group_a: baseline group value (the "from").
        group_b: comparison group value (the "to").
        agg: how to aggregate within each group — one of SUPPORTED_AGGS.

    Returns:
        dict with:
            dimension, value_column, agg  — echo of the chosen settings
            group_a, value_a              — baseline group + its aggregate
            group_b, value_b              — comparison group + its aggregate
            diff_abs                      — value_b - value_a
            diff_pct                      — diff_abs / value_a, or None if value_a == 0
            comparison                    — "higher" | "lower" | "equal" (b relative to a)

    Raises:
        ColumnNotFoundError: if dimension or value_column is absent.
        UnsupportedAggregationError: if agg is not supported.
        GroupNotFoundError: if group_a or group_b has no rows.
    """
    missing = [c for c in (dimension, value_column) if c not in df.columns]
    if missing:
        raise ColumnNotFoundError(f"columns not found: {missing}")
    if agg not in SUPPORTED_AGGS:
        raise UnsupportedAggregationError(
            f"unsupported aggregation '{agg}'; supported: {list(SUPPORTED_AGGS)}"
        )

    rows_a = df.loc[df[dimension] == group_a, value_column]
    rows_b = df.loc[df[dimension] == group_b, value_column]
    empty = [g for g, rows in ((group_a, rows_a), (group_b, rows_b)) if rows.empty]
    if empty:
        raise GroupNotFoundError(
            f"no rows for group(s) {empty} in column '{dimension}'"
        )

    value_a = float(rows_a.agg(agg))
    value_b = float(rows_b.agg(agg))
    diff_abs = value_b - value_a
    diff_pct = (diff_abs / value_a) if value_a != 0 else None

    if diff_abs > 0:
        comparison = "higher"
    elif diff_abs < 0:
        comparison = "lower"
    else:
        comparison = "equal"

    return {
        "dimension": dimension,
        "value_column": value_column,
        "agg": agg,
        "group_a": group_a,
        "value_a": value_a,
        "group_b": group_b,
        "value_b": value_b,
        "diff_abs": diff_abs,
        "diff_pct": diff_pct,
        "comparison": comparison,
    }