"""Time series figure factories for Toronto housing data."""

from typing import Any

import plotly.express as px
import plotly.graph_objects as go


def create_price_time_series(
    data: list[dict[str, Any]],
    date_column: str = "full_date",
    price_column: str = "avg_price",
    group_column: str | None = None,
    title: str = "Average Price Over Time",
    show_yoy: bool = True,
) -> go.Figure:
    """Create a time series chart for price data.

    Args:
        data: List of records with date and price columns.
        date_column: Column name for dates.
        price_column: Column name for price values.
        group_column: Optional column for grouping (e.g., district_code).
        title: Chart title.
        show_yoy: Whether to show year-over-year change annotations.

    Returns:
        Plotly Figure object.
    """
    import pandas as pd

    if not data:
        fig = go.Figure()
        fig.add_annotation(
            text="No data available",
            xref="paper",
            yref="paper",
            x=0.5,
            y=0.5,
            showarrow=False,
        )
        fig.update_layout(title=title, height=350)
        return fig

    df = pd.DataFrame(data)
    df[date_column] = pd.to_datetime(df[date_column])

    if group_column and group_column in df.columns:
        fig = px.line(
            df,
            x=date_column,
            y=price_column,
            color=group_column,
            title=title,
        )
    else:
        fig = px.line(
            df,
            x=date_column,
            y=price_column,
            title=title,
        )

    fig.update_layout(
        height=350,
        margin={"l": 40, "r": 20, "t": 50, "b": 40},
        xaxis_title="Date",
        yaxis_title=price_column.replace("_", " ").title(),
        yaxis_tickprefix="$",
        yaxis_tickformat=",",
        hovermode="x unified",
    )

    return fig


def create_volume_time_series(
    data: list[dict[str, Any]],
    date_column: str = "full_date",
    volume_column: str = "sales_count",
    group_column: str | None = None,
    title: str = "Sales Volume Over Time",
    chart_type: str = "bar",
) -> go.Figure:
    """Create a time series chart for volume/count data.

    Args:
        data: List of records with date and volume columns.
        date_column: Column name for dates.
        volume_column: Column name for volume values.
        group_column: Optional column for grouping.
        title: Chart title.
        chart_type: 'bar' or 'line'.

    Returns:
        Plotly Figure object.
    """
    import pandas as pd

    if not data:
        fig = go.Figure()
        fig.add_annotation(
            text="No data available",
            xref="paper",
            yref="paper",
            x=0.5,
            y=0.5,
            showarrow=False,
        )
        fig.update_layout(title=title, height=350)
        return fig

    df = pd.DataFrame(data)
    df[date_column] = pd.to_datetime(df[date_column])

    if chart_type == "bar":
        if group_column and group_column in df.columns:
            fig = px.bar(
                df,
                x=date_column,
                y=volume_column,
                color=group_column,
                title=title,
            )
        else:
            fig = px.bar(
                df,
                x=date_column,
                y=volume_column,
                title=title,
            )
    else:
        if group_column and group_column in df.columns:
            fig = px.line(
                df,
                x=date_column,
                y=volume_column,
                color=group_column,
                title=title,
            )
        else:
            fig = px.line(
                df,
                x=date_column,
                y=volume_column,
                title=title,
            )

    fig.update_layout(
        height=350,
        margin={"l": 40, "r": 20, "t": 50, "b": 40},
        xaxis_title="Date",
        yaxis_title=volume_column.replace("_", " ").title(),
        yaxis_tickformat=",",
        hovermode="x unified",
    )

    return fig


def create_market_comparison_chart(
    data: list[dict[str, Any]],
    date_column: str = "full_date",
    metrics: list[str] | None = None,
    title: str = "Market Indicators",
) -> go.Figure:
    """Create a multi-metric comparison chart.

    Args:
        data: List of records with date and metric columns.
        date_column: Column name for dates.
        metrics: List of metric columns to display.
        title: Chart title.

    Returns:
        Plotly Figure object with secondary y-axis.
    """
    import pandas as pd
    from plotly.subplots import make_subplots

    if not data:
        fig = go.Figure()
        fig.add_annotation(
            text="No data available",
            xref="paper",
            yref="paper",
            x=0.5,
            y=0.5,
            showarrow=False,
        )
        fig.update_layout(title=title, height=400)
        return fig

    if metrics is None:
        metrics = ["avg_price", "sales_count"]

    df = pd.DataFrame(data)
    df[date_column] = pd.to_datetime(df[date_column])

    fig = make_subplots(specs=[[{"secondary_y": True}]])

    colors = ["#1f77b4", "#ff7f0e", "#2ca02c", "#d62728"]

    for i, metric in enumerate(metrics[:4]):
        if metric not in df.columns:
            continue

        secondary = i > 0
        fig.add_trace(
            go.Scatter(
                x=df[date_column],
                y=df[metric],
                name=metric.replace("_", " ").title(),
                line={"color": colors[i % len(colors)]},
            ),
            secondary_y=secondary,
        )

    fig.update_layout(
        title=title,
        height=400,
        margin={"l": 40, "r": 40, "t": 50, "b": 40},
        hovermode="x unified",
        legend={
            "orientation": "h",
            "yanchor": "bottom",
            "y": 1.02,
            "xanchor": "right",
            "x": 1,
        },
    )

    return fig