"""Common plotting utilities for experiments."""

import matplotlib.pyplot as plt
import seaborn as sns
import numpy as np
import pandas as pd


def setup_plot_style():
    """Set up default plotting style."""
    plt.style.use('seaborn-v0_8-whitegrid')
    sns.set_palette("husl")
    plt.rcParams['figure.figsize'] = (12, 6)
    plt.rcParams['font.size'] = 10


def plot_ic_series(ic_by_date: pd.Series, title: str = "IC Over Time",
                   figsize: tuple = (14, 4)) -> plt.Figure:
    """Plot IC time series with rolling mean.

    Args:
        ic_by_date: Series with datetime index and IC values
        title: Plot title
        figsize: Figure size

    Returns:
        Matplotlib figure
    """
    fig, ax = plt.subplots(figsize=figsize)

    # Plot raw IC
    ax.plot(ic_by_date.index, ic_by_date.values, alpha=0.5, color='gray', label='Daily IC')

    # Plot rolling mean
    rolling = ic_by_date.rolling(20, min_periods=5).mean()
    ax.plot(rolling.index, rolling.values, color='blue', linewidth=2, label='20-day MA')

    # Add mean line
    mean_ic = ic_by_date.mean()
    ax.axhline(y=mean_ic, color='red', linestyle='--',
               label=f'Mean IC: {mean_ic:.4f}')
    ax.axhline(y=0, color='black', linestyle='-', alpha=0.3)

    ax.set_title(title)
    ax.set_xlabel('Date')
    ax.set_ylabel('Information Coefficient')
    ax.legend(loc='upper right')

    plt.tight_layout()
    return fig


def plot_cumulative_returns(returns: pd.Series, title: str = "Cumulative Returns",
                           figsize: tuple = (12, 6)) -> plt.Figure:
    """Plot cumulative returns.

    Args:
        returns: Series with datetime index and daily returns
        title: Plot title
        figsize: Figure size

    Returns:
        Matplotlib figure
    """
    fig, ax = plt.subplots(figsize=figsize)

    cumulative = (1 + returns).cumprod()
    ax.plot(cumulative.index, cumulative.values, linewidth=1.5)

    ax.set_title(title)
    ax.set_xlabel('Date')
    ax.set_ylabel('Cumulative Return')
    ax.set_yscale('log')

    # Add final return annotation
    final_return = cumulative.iloc[-1] - 1
    ax.annotate(f'{final_return:.2%}',
                xy=(cumulative.index[-1], cumulative.iloc[-1]),
                xytext=(10, 0), textcoords='offset points',
                fontsize=10, color='green' if final_return > 0 else 'red')

    plt.tight_layout()
    return fig


def plot_factor_distribution(factor: pd.Series, title: str = "Factor Distribution",
                             figsize: tuple = (10, 6)) -> plt.Figure:
    """Plot factor distribution with statistics.

    Args:
        factor: Series of factor values
        title: Plot title
        figsize: Figure size

    Returns:
        Matplotlib figure
    """
    fig, axes = plt.subplots(1, 2, figsize=figsize)

    # Histogram
    axes[0].hist(factor.dropna(), bins=100, alpha=0.7, edgecolor='black')
    axes[0].set_title(f'{title} - Distribution')
    axes[0].set_xlabel('Value')
    axes[0].set_ylabel('Frequency')

    # Q-Q plot
    from scipy import stats
    stats.probplot(factor.dropna(), dist="norm", plot=axes[1])
    axes[1].set_title(f'{title} - Q-Q Plot')

    # Add statistics text
    stats_text = f"Mean: {factor.mean():.4f}\nStd: {factor.std():.4f}\n"
    stats_text += f"Skew: {factor.skew():.4f}\nKurt: {factor.kurtosis():.4f}"
    axes[0].text(0.95, 0.95, stats_text, transform=axes[0].transAxes,
                 verticalalignment='top', horizontalalignment='right',
                 bbox=dict(boxstyle='round', facecolor='wheat', alpha=0.5))

    plt.tight_layout()
    return fig