import sqlite3
from pathlib import Path
import sys

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns
from statsmodels.nonparametric.smoothers_lowess import lowess

sns.set_theme(style="whitegrid")
plt.rcParams["figure.figsize"] = (10, 6)

project_root = Path(__file__).resolve().parent.parent
sys.path.append(str(project_root / "preanalysis_old_bad"))
import eda_utils as eda  # noqa: E402

DB_PATH = project_root / "dataset" / "ds.sqlite"
OUT_DIR = project_root / "main_hypot"
X_COL = "avg_imp_per_day"
Y_COL = "orders_amt_total"
X_MAX = 18  # обрезаем длинный хвост по показам, чтобы облака было легче читать
SCATTER_COLOR = "#2c7bb6"


def load_client_level(db_path: Path) -> pd.DataFrame:
    """Собирает агрегаты по клиентам без усреднения по x."""
    conn = sqlite3.connect(db_path)
    df = pd.read_sql_query("select * from communications", conn, parse_dates=["business_dt"])
    conn.close()

    for cols, name in [
        (eda.ACTIVE_IMP_COLS, "active_imp_total"),
        (eda.PASSIVE_IMP_COLS, "passive_imp_total"),
        (eda.ACTIVE_CLICK_COLS, "active_click_total"),
        (eda.PASSIVE_CLICK_COLS, "passive_click_total"),
        (eda.ORDER_COLS, "orders_amt_total"),
    ]:
        df[name] = df[cols].sum(axis=1)

    df["imp_total"] = df["active_imp_total"] + df["passive_imp_total"]

    client = (
        df.groupby("id")
        .agg(
            imp_total=("imp_total", "sum"),
            orders_amt_total=("orders_amt_total", "sum"),
            contact_days=("business_dt", "nunique"),
        )
        .reset_index()
    )

    client[X_COL] = eda.safe_divide(client["imp_total"], client["contact_days"])
    client[Y_COL] = client["orders_amt_total"]
    client = client[["id", X_COL, Y_COL]].dropna()

    in_range = client[client[X_COL] <= X_MAX].copy()
    print(f"Loaded {len(client)} clients; {len(in_range)} within x<={X_MAX} kept for plotting.")
    return in_range


def remove_outliers(df: pd.DataFrame, iqr_k: float = 1.5) -> pd.DataFrame:
    """Убирает выбросы по IQR отдельно по x и y."""
    def bounds(series: pd.Series) -> tuple[float, float]:
        q1, q3 = series.quantile([0.05, 0.95])
        iqr = q3 - q1
        return q1 - iqr_k * iqr, q3 + iqr_k * iqr

    x_low, x_high = bounds(df[X_COL])
    y_low, y_high = bounds(df[Y_COL])
    filtered = df[
        df[X_COL].between(max(0, x_low), x_high)
        & df[Y_COL].between(max(0, y_low), y_high)
    ].copy()
    print(f"Outlier cleaning: {len(df)} -> {len(filtered)} points (IQR k={iqr_k}).")
    return filtered


def plot_density_scatter(
    df: pd.DataFrame,
    title: str,
    out_name: str,
    with_trend: bool = False,
    alpha: float = 0.08,
) -> None:
    fig, ax = plt.subplots(figsize=(10, 6))
    sns.scatterplot(
        data=df,
        x=X_COL,
        y=Y_COL,
        color=SCATTER_COLOR,
        s=20,
        alpha=alpha,
        linewidth=0,
        ax=ax,
    )

    if with_trend:
        trend = lowess(df[Y_COL], df[X_COL], frac=0.3, return_sorted=True)
        ax.plot(trend[:, 0], trend[:, 1], color="red", linewidth=2.5, label="LOWESS тренд")
        ax.legend()

    ax.set_xlim(0, X_MAX)
    ax.set_ylim(bottom=0)
    ax.set_xlabel("Среднее число показов в день")
    ax.set_ylabel("Число заказов за период (сумма)")
    ax.set_title(title)
    ax.grid(alpha=0.3)

    OUT_DIR.mkdir(parents=True, exist_ok=True)
    out_path = OUT_DIR / out_name
    fig.tight_layout()
    fig.savefig(out_path, dpi=150)
    plt.close(fig)
    print(f"Saved {out_path}")


def main() -> None:
    client = load_client_level(DB_PATH)

    plot_density_scatter(
        client,
        title="Облако: заказы vs средние показы в день (все клиенты)",
        out_name="orders_vs_avg_imp_scatter.png",
    )

    cleaned = remove_outliers(client)
    plot_density_scatter(
        cleaned,
        title="Облако без выбросов (IQR) заказы vs средние показы в день",
        out_name="orders_vs_avg_imp_scatter_clean.png",
    )

    plot_density_scatter(
        cleaned,
        title="Облако без выбросов + тренд",
        out_name="orders_vs_avg_imp_scatter_trend.png",
        with_trend=True,
        alpha=0.1,
    )


if __name__ == "__main__":
    main()