import matplotlib.pyplot as plt
from latencyData import LatencyData
from latencyArgs import get_args
import logging


def plot_delay_histogram_with_ci(l: LatencyData, save_to_file=True, show_plot=False, title="default"):
    """
    Универсальный скрипт для построения гистограммы с доверительным интервалом
    """
    try:
        plt.rcParams.update({'font.size': 18})
        # Создание гистограммы
        plt.figure(figsize=(14, 8))
        n_bins = min(30, len(l.delays) // 5)

        n, bins, patches = plt.hist(l.delays, bins=n_bins, alpha=0.7, color='steelblue',
                                    edgecolor='black', linewidth=0.5, density=False)

        plt.xlabel('Задержка (мс)')
        plt.ylabel('Частота')
        plt.title(f'Распределение задержек на обработку нажатия мыши {title} (n={len(l.delays)}, CI={l.confidence_level*100}%)',
                  fontsize=14, fontweight='bold')
        plt.grid(True, alpha=0.3)

        l.mean_delay = float(l.mean_delay)
        l.median_delay = float(l.median_delay)
        # Добавление статистических линий
        plt.axvline(l.mean_delay, color='red', linestyle='-', linewidth=2,
                    label=f'Среднее ({l.mean_delay:.1f} мс)')
        plt.axvline(l.median_delay, color='green', linestyle='-', linewidth=2,
                    label=f'Медиана ({l.median_delay:.1f} мс)')

        # Добавление доверительного интервала
        plt.axvspan(l.ci_lower, l.ci_upper, alpha=0.2, color='yellow',
                    label=f'ДИ {l.confidence_level*100}%: [{l.ci_lower:.1f}, {l.ci_upper:.1f}] мс')

        # Вертикальные линии для границ доверительного интервала
        plt.axvline(l.ci_lower, color='orange',
                    linestyle='--', linewidth=1, alpha=0.7)
        plt.axvline(l.ci_upper, color='orange',
                    linestyle='--', linewidth=1, alpha=0.7)

        plt.legend(loc='upper right')
        plt.tight_layout()

        # Сохранение в файл
        if save_to_file:
            output_file = f'{get_args().img_prefix}/delay_distribution_{title}_ci_{int(l.confidence_level*100)}.png'
            plt.savefig(output_file, dpi=300, bbox_inches='tight')
            print(f"\n✓ Гистограмма сохранена как: {output_file}")

        # Показ графика
        if show_plot:
            plt.show()
        else:
            plt.close()

        print("\n✓ Анализ завершен успешно")

        return {
            'data': l.delays,
            'mean': l.mean_delay,
            'median': l.median_delay,
            'std': l.std_delay,
            'ci_lower': l.ci_lower,
            'ci_upper': l.ci_upper,
            'confidence_level': l.confidence_level
        }
    except Exception as e:
        print(f"✗ Ошибка: {e}")
        return None


def plot_horizontal_hist_with_ci(latencies: dict, colors: dict, labels: dict, save_to_file=True, show_plot=True, title="default"):
    """
    Parameters
    ----------
    latencies: dict
        key is a filename, value is a latencyData object

    colors: dict
        key is a filename, value is string in format tab:<color>

    labels: dict
        key is a filename, value is a string label

    """
    plt.rcParams.update({'font.size': 18})
    # Создание гистограммы
    fig, ax = plt.subplots(figsize=(12, 6))

    keys = latencies.keys()
    values = latencies.values()

    medians = [i.mean_delay for i in values]
    errors = [i.err for i in values]
    y_idx = range(len(keys))
    logging.info(f"medians={medians}; errors={errors}")
    # sorting of all arrays to have same order of elements
    bar_colors = [colors[key] for key in keys]
    bar_labels = [labels[key] for key in keys]
    bar_mlabels = []
    for key in keys:
        bar_mlabels.append(
            f"{latencies[key].mean_delay:.5g}±{latencies[key].err:.3g}")

    logging.info(bar_mlabels)
    cont = ax.barh(y_idx, medians, xerr=errors,
                   color=bar_colors, capsize=15, align='center')
    ax.bar_label(cont, labels=bar_mlabels, label_type='center')
    logging.info(f"y_idx={y_idx}; bar_labels={bar_labels}")
    ax.set_yticks(y_idx, bar_labels)
    ax.set_xlabel('Задержка (мс)')
    fig.tight_layout()

    if show_plot:
        plt.show()
    else:
        plt.close()

    if save_to_file:
        fig.savefig(f'{get_args().img_prefix}/delay_dist_overall_{title}')


def plot_point_cloud(latencies: dict,
                     colors: dict,
                     labels: dict,
                     dT: float,
                     hlines=dict,
                     save_to_file=True,
                     show_plot=True,
                     title="default"):
    """
    Parameters
    ----------
    latencies: dict
        key is a filename, value is a latencyData object

    colors: dict
        key is a filename, value is string in format tab:<color>

    labels: dict
        key is a filename, value is a string label

    """

    plt.rcParams.update({'font.size': 16})
    # Создание гистограммы
    fig, ax = plt.subplots(figsize=(14, 10))

    keys = latencies.keys()
    values = latencies.values()

    medians = [i.mean_delay for i in values]

    # sorting of all arrays to have same order of elements
    point_labels = [labels[key] for key in keys]
    point_colors = [colors[key] for key in keys]
    point_xminall = min(len(value.delays) for value in values)
    point_values = [value.delays[:point_xminall] for value in values]
    point_x = list(range(point_xminall))
    point_x = [float(x) * dT for x in point_x]
    point_ld = [value for value in values]
    for i in range(len(point_values)):
        ax.plot(point_x,
                point_values[i],
                color=point_colors[i],
                label=point_labels[i],
                marker='o',
                linestyle='None')
        ax.axhline(y=medians[i],
                   color=point_colors[i],
                   linestyle='--')
        ax.axhspan(point_ld[i].ci_lower,
                   point_ld[i].ci_upper,
                   alpha=0.2,
                   color=point_colors[i])
    ax.legend(ncol=3, bbox_to_anchor=(0.85, 1.15), fancybox=True)
    ax.set_xlabel('Время (секунды)')
    ax.set_ylabel('Задержка (мс)')

    fig.tight_layout()

    if show_plot:
        plt.show()
    else:
        plt.close()

    if save_to_file:
        fig.savefig(f'{get_args().img_prefix}/delay_dist_cloud_{title}')