import numpy


# 字典排序
def dict_sort(_dict, _sort_by='key', _reverse=False):
    if _sort_by == 'key':
        _result = sorted(_dict.items(), key=lambda x: x[0], reverse=_reverse)
    else:
        _result = sorted(_dict.items(), key=lambda x: x[1], reverse=_reverse)
    _result_dict = dict()

    for r in _result:
        _result_dict[r[0]] = r[1]
    return _result_dict


# 计算偏差值
def get_analysis_data(_raw_dict):
    total = 0
    for _num in _raw_dict.values():
        total += _num
    average = total / len(_raw_dict.keys())
    _delta_dict = dict()
    for _key, _val in _raw_dict.items():
        _delta_dict[_key] = round(_raw_dict[_key] - average, 2)
    return _delta_dict


def print_data(_data_list):
    new_data_list = [list(d.items()) for d in _data_list]
    for i in range(len(new_data_list[0])):
        print_str = f""
        for data in new_data_list:
            print_str += f"{data[i][0]}  [{data[i][1]}]\t"
        print(print_str)


def new_num(min_num, max_num, random_num):
    num = {}

    d = [i for i in range(min_num, max_num + 1)]

    n = 100000
    while n > 0:
        result = numpy.random.choice(d, random_num, replace=False)
        # print(sorted(r))
        for r in result:
            if r in num.keys():
                num[r] += 1
            else:
                num[r] = 1
        n -= 1

    sorted_result = sorted(num.items(), key=lambda x: x[0])
    red_result_dict = {}
    for item in sorted_result:
        red_result_dict[item[0]] = item[1]
    # print(red_result_dict)
    sorted_result_by_value = sorted(num.items(), key=lambda x: x[1], reverse=True)
    # print(sorted_result_by_value)
    return sorted_result_by_value