from __future__ import annotations

from datetime import date, timedelta
from typing import Any

from dashboard_data.card_utils import PERSIAN_WEEKDAYS


INTERPOLATION_LIMIT = 3
QUALITY_REAL = "REAL"
QUALITY_INTERPOLATED = "INTERPOLATED"
QUALITY_MISSING = "MISSING"


def _day_categories() -> list[date]:
    return [date.today() - timedelta(days=offset) for offset in range(6, -1, -1)]


def _day_label(day: date) -> str:
    return PERSIAN_WEEKDAYS[day.weekday()]


def _history_value_map(history: list[Any], field_name: str) -> dict[date, float | None]:
    value_map: dict[date, float | None] = {}
    for item in history:
        timestamp = getattr(item, "recorded_at", None)
        if timestamp is None:
            continue
        day = timestamp.date()
        if day in value_map:
            continue
        value = getattr(item, field_name, None)
        value_map[day] = float(value) if value is not None else None
    return value_map


def _apply_linear_interpolation(points: list[dict[str, Any]], limit: int = INTERPOLATION_LIMIT) -> list[dict[str, Any]]:
    output = [dict(point) for point in points]
    known_indexes = [index for index, point in enumerate(output) if point["value"] is not None]

    for start_index, end_index in zip(known_indexes, known_indexes[1:]):
        gap = end_index - start_index - 1
        if gap <= 0 or gap > limit:
            continue

        start_value = output[start_index]["value"]
        end_value = output[end_index]["value"]
        if start_value is None or end_value is None:
            continue

        step = (end_value - start_value) / (gap + 1)
        for offset in range(1, gap + 1):
            target_index = start_index + offset
            output[target_index]["value"] = round(start_value + (step * offset), 2)
            output[target_index]["quality_flag"] = QUALITY_INTERPOLATED

    return output


def _build_week_points(
    history: list[Any],
    field_name: str,
    day_offset_start: int,
) -> list[dict[str, Any]]:
    days = [date.today() - timedelta(days=offset) for offset in range(day_offset_start + 6, day_offset_start - 1, -1)]
    value_map = _history_value_map(history, field_name)
    raw_points = [
        {
            "timestamp": day.isoformat(),
            "value": round(value_map[day], 2) if day in value_map and value_map[day] is not None else None,
            "quality_flag": QUALITY_REAL if day in value_map and value_map[day] is not None else QUALITY_MISSING,
        }
        for day in days
    ]
    return _apply_linear_interpolation(raw_points)


def _average_known(points: list[dict[str, Any]]) -> float | None:
    values = [point["value"] for point in points if point["value"] is not None]
    if not values:
        return None
    return sum(values) / len(values)


def build_sensor_comparison_chart(sensor_id: str, context: dict | None = None, ai_bundle: dict | None = None) -> dict:
    history = (context or {}).get("history", [])
    current_sensor = (context or {}).get("sensor")
    current_value = getattr(current_sensor, "soil_moisture", None) if current_sensor is not None else None
    current_value = round(float(current_value), 2) if current_value is not None else None

    this_week_points = _build_week_points(history, "soil_moisture", day_offset_start=0)
    last_week_points = _build_week_points(history, "soil_moisture", day_offset_start=7)

    this_week_avg = _average_known(this_week_points)
    last_week_avg = _average_known(last_week_points)
    delta = 0
    if this_week_avg is not None and last_week_avg not in (None, 0):
        delta = round(((this_week_avg - last_week_avg) / last_week_avg) * 100)

    categories = [_day_label(day) for day in _day_categories()]

    return {
        "currentValue": current_value,
        "currentValueQuality": QUALITY_REAL if current_value is not None else QUALITY_MISSING,
        "vsLastWeek": f"{'+' if delta >= 0 else ''}{delta}%",
        "vsLastWeekValue": delta,
        "categories": categories,
        "series": [
            {
                "name": "هفته جاری",
                "data": [point["value"] for point in this_week_points],
                "points": this_week_points,
            },
            {
                "name": "هفته قبل",
                "data": [point["value"] for point in last_week_points],
                "points": last_week_points,
            },
        ],
        "qualityLegend": {
            QUALITY_REAL: "اندازه‌گیری واقعی سنسور",
            QUALITY_INTERPOLATED: "برآورد خطی برای شکاف کوتاه داده",
            QUALITY_MISSING: "داده معتبر در دسترس نیست",
        },
    }