import pandas as pd
import altair as alt

from src.utils import PERIODS, CURRENT_YEAR, UNIT_NAME_COL


def build_trend_chart(trend_df: pd.DataFrame, species_name: str,
                      year_from: int = None, year_to: int = None) -> alt.Chart:
    base = alt.Chart(trend_df).encode(
        x=alt.X("year:O", title="Jahr", axis=alt.Axis(labelAngle=-45)),
        y=alt.Y("count:Q", title="Anzahl Funde", scale=alt.Scale(zero=True)),
        tooltip=[
            alt.Tooltip("year:O", title="Jahr"),
            alt.Tooltip("count:Q", title="Funde"),
        ],
    )

    line = base.mark_line(color="#00cc6a", strokeWidth=2)
    points = base.mark_circle(color="#00cc6a", size=40)

    current_data = trend_df[trend_df["year"] == CURRENT_YEAR]
    current_point = alt.Chart(current_data).mark_circle(
        color="#ff6b35", size=80
    ).encode(
        x=alt.X("year:O"),
        y=alt.Y("count:Q"),
        tooltip=[alt.Tooltip("year:O", title="Jahr"), alt.Tooltip("count:Q", title="Funde")],
    )

    y_label = f"{year_from or PERIODS[0][0]}–{year_to or CURRENT_YEAR}"
    chart = (line + points + current_point).properties(
        title=alt.TitleParams(
            text=f"Fundtrend: {species_name}",
            subtitle=f"Jährliche GBIF-Funde {y_label} | Rot = aktuelles Jahr",
            fontSize=14,
            subtitleFontSize=11,
        ),
        height=300,
    ).interactive()

    return chart


def prepare_per_unit_data(
    df: pd.DataFrame,
    unit_col: str,
    max_units: int = 20,
) -> tuple[list[str], pd.DataFrame]:
    """Return (ordered list of top unit names, aggregated year/count DataFrame).

    The returned DataFrame has columns [unit_col, 'year', 'count'].
    Units are ordered by total descending.
    """
    if unit_col not in df.columns or "year" not in df.columns:
        return [], pd.DataFrame()

    data = (
        df.dropna(subset=["year", unit_col])
        .assign(year=lambda d: d["year"].astype(int))
        .groupby([unit_col, "year"])
        .size()
        .reset_index(name="count")
    )

    if data.empty:
        return [], pd.DataFrame()

    top_units = (
        data.groupby(unit_col)["count"].sum()
        .nlargest(max_units).index.tolist()
    )
    data = data[data[unit_col].isin(top_units)].copy()
    return top_units, data


def build_unit_row_chart(unit_data: pd.DataFrame,
                         y_max: int | None = None) -> alt.Chart:
    """Full-width line chart for a single geographic unit, same style as overall trend.

    y_max: shared Y-axis maximum across all unit charts for comparability.
    """
    y_scale = alt.Scale(zero=True, domain=[0, y_max]) if y_max is not None else alt.Scale(zero=True)

    base = alt.Chart(unit_data).encode(
        x=alt.X("year:O", title="Jahr", axis=alt.Axis(labelAngle=-45)),
        y=alt.Y("count:Q", title="Funde", scale=y_scale),
        tooltip=[
            alt.Tooltip("year:O", title="Jahr"),
            alt.Tooltip("count:Q", title="Funde"),
        ],
    )

    line = base.mark_line(color="#00cc6a", strokeWidth=2)
    points = base.mark_circle(color="#00cc6a", size=40)

    current_data = unit_data[unit_data["year"] == CURRENT_YEAR]
    current_point = alt.Chart(current_data).mark_circle(
        color="#ff6b35", size=80
    ).encode(
        x=alt.X("year:O"),
        y=alt.Y("count:Q", scale=y_scale),
        tooltip=[alt.Tooltip("year:O", title="Jahr"), alt.Tooltip("count:Q", title="Funde")],
    )

    return (line + points + current_point).properties(
        height=140,
    ).interactive()


def compute_trend_from_df(df: pd.DataFrame) -> pd.DataFrame:
    """Count occurrences per year from any DataFrame with a 'year' column."""
    if df.empty or "year" not in df.columns:
        return pd.DataFrame(columns=["year", "count"])
    counts = (
        df.dropna(subset=["year"])
        .assign(year=lambda d: d["year"].astype(int))
        .groupby("year")
        .size()
        .reset_index(name="count")
        .sort_values("year")
    )
    return counts