from __future__ import annotations

import math
from pathlib import Path
from typing import Any

import numpy as np
import pandas as pd

COORD_COLUMN_NAMES = {
    "lat",
    "latitude",
    "siat_latitude",
    "lon",
    "long",
    "longitude",
    "siat_longitude",
}


def _is_coordinate_column(col_name: Any) -> bool:
    return str(col_name).strip().lower() in COORD_COLUMN_NAMES


def sanitize_value(value: Any) -> Any:
    if isinstance(value, np.ndarray):
        return [sanitize_value(item) for item in value.tolist()]
    if isinstance(value, np.generic):
        return sanitize_value(value.item())
    if isinstance(value, (np.floating, float)):
        if math.isnan(value) or math.isinf(value):
            return None
        return float(value)
    if isinstance(value, (np.integer, int)):
        return int(value)
    if isinstance(value, (np.bool_, bool)):
        return bool(value)
    if isinstance(value, pd.Timestamp):
        return value.isoformat()
    if isinstance(value, pd.Timedelta):
        return str(value)
    if isinstance(value, pd.Series):
        return [sanitize_value(item) for item in value.tolist()]
    if isinstance(value, pd.Index):
        return [sanitize_value(item) for item in value.tolist()]
    if isinstance(value, pd.DataFrame):
        return sanitize_value(value.to_dict(orient="records"))
    if isinstance(value, Path):
        return str(value)
    if value is None:
        return None
    if isinstance(value, str):
        return value
    if isinstance(value, dict):
        return {str(k): sanitize_value(v) for k, v in value.items()}
    if isinstance(value, (list, tuple, set)):
        return [sanitize_value(v) for v in value]
    try:
        if pd.isna(value):
            return None
    except Exception:
        pass
    return value


def dataframe_to_payload(
    df: pd.DataFrame | None,
    decimals: int | None = None,
    max_rows: int | None = None,
) -> dict[str, Any] | None:
    if df is None:
        return None

    # Evita cópia quando não há transformação necessária.
    df_work = df if decimals is None else df.copy()
    if decimals is not None:
        numeric_cols = [
            col
            for col in df_work.select_dtypes(include=[np.number]).columns
            if not _is_coordinate_column(col)
        ]
        if numeric_cols:
            df_work.loc[:, numeric_cols] = df_work.loc[:, numeric_cols].round(decimals)

    total_rows = len(df_work)
    # Regra de integridade: payloads nunca devem suprimir linhas.
    truncated = False

    columns = [str(c) for c in df_work.columns]
    rows: list[dict[str, Any]] = []
    # itertuples é significativamente mais leve que iterrows para payloads grandes.
    for tuple_row in df_work.itertuples(index=True, name=None):
        payload_row = {"_index": sanitize_value(tuple_row[0])}
        for col, value in zip(columns, tuple_row[1:]):
            payload_row[col] = sanitize_value(value)
        rows.append(payload_row)

    columns_payload = ["_index"] + columns
    return {
        "columns": columns_payload,
        "rows": rows,
        "total_rows": total_rows,
        "returned_rows": len(rows),
        "truncated": truncated,
    }


def figure_to_payload(fig: Any) -> dict[str, Any] | None:
    if fig is None:
        return None
    try:
        return sanitize_value(fig.to_plotly_json())
    except Exception:
        return None