import pandas as pd
import ast
from collections import Counter

################################ 计算概率，排序 ##############################
def statistical_frequency(combined_df):
    
    percentages_list = []
    if  'zhenshi' in combined_df.colums:
        zhenshi = combined_df['zhenshi']
        combined_df = combined_df.drop(columns=['zhenshi'])
        
    for i in range(len(combined_df)):
        row = combined_df.iloc[i]
        all_beam = []

        # 遍历当前行的每列，收集所有元素
        for j in range(len(row)):
            # 检查 row[j] 是否为字符串，必要时转换为列表
            if isinstance(row[j], str):
                try:
                    aa = ast.literal_eval(row[j])  # 转换字符串为列表
                except (ValueError, SyntaxError):
                    aa = []  # 如果无法解析，设置为空列表
            else:
                aa = row[j] if isinstance(row[j], list) else []  # 如果是列表，直接使用

            all_beam.extend(sorted(set(aa)))

        # 统计频率并计算比例
        frequencies = Counter(all_beam)
        total_elements = len(combined_df.columns)  # 用总元素数而非列数作为分母
        if total_elements > 0:
            percentage = {key: value / total_elements for key, value in frequencies.items()}

        # 对比例从高到低排序
        sorted_percentage = dict(sorted(percentage.items(), key=lambda item: item[1], reverse=True))

        # 将排序后的结果添加到列表
        percentages_list.append(sorted_percentage)

    # 将计算好的 percentages_list 添加为 DataFrame 的新列
    combined_df['frequency'] = percentages_list
    combined_df['zhenshi'] = zhenshi
    
    return combined_df



###################################### 根据值查找键 ##########################
def keys_valus(tgt_dict, key):
    value = [k for k, v in tgt_dict.items() if v == key]
    return value

# 将张量转换为营养成分
def medium(b, tgt_dict):
    l = []
    for i in b:
        tensor = i
        l.extend(keys_valus(tgt_dict, tensor))
        l = [item for item in l if item not in ['</s>', '<s>', 'blank']]
    return l