"""
profiler.py - 性能分析
"""
import time
import cProfile
import pstats
import io
from main import search_videos, get_cid, get_danmaku, parse_danmaku
from analyse import filter_danmakus, count_danmakus, get_top_danmakus
from visualization import (filter_relevant_danmakus, preprocess_danmakus,
                          extract_words, remove_stopwords,
                          calculate_word_frequency, generate_wordcloud)

def run_performance_analysis():
    """运行性能分析并记录结果。"""
    profiler = cProfile.Profile()
    profiler.enable()  # 开始性能分析

    # 固定参数
    keyword = "python"
    max_results = 5
    stopwords = ['的', '是', '在', '有', '和', '这', '了', '与']

    # 获取视频列表
    video_list = search_videos(keyword, max_results)
    for video_id in video_list:
        cid = get_cid(video_id)
        danmakus = get_danmaku(video_id, cid)
        xml_content = parse_danmaku(danmakus)  # 假设 get_danmaku 返回 XML
        time.sleep(2)

    # 处理弹幕
    filtered_danmakus = filter_danmakus(xml_content, keyword)
    counted_danmakus = count_danmakus(filtered_danmakus)
    get_top_danmakus(counted_danmakus)

    # 进一步分析
    relevant_danmakus = filter_relevant_danmakus(xml_content, keyword)
    preprocessed_danmakus = preprocess_danmakus(relevant_danmakus)
    words = extract_words(preprocessed_danmakus)
    words_no_stopwords = remove_stopwords(words, stopwords)
    word_freq = calculate_word_frequency(words_no_stopwords)
    generate_wordcloud(word_freq)

    profiler.disable()  # 停止性能分析

    # 将性能分析结果输出到一个字符串流中
    output_stream = io.StringIO()
    sortby = pstats.SortKey.CUMULATIVE  # 按照累计时间排序
    stats = pstats.Stats(profiler, stream=output_stream).sort_stats(sortby)
    stats.print_stats()  # 打印结果

    # 输出性能分析结果
    with open("profiler.txt", "w", encoding='utf-8') as output_file:
        output_file.write(output_stream.getvalue())

    # 保存性能分析数据以便使用 SnakeViz 可视化
    profiler.dump_stats("output.prof")

if __name__ == "__main__":
    run_performance_analysis()