优化代码def batch_analysis(base_info): """ 算法模块调用函数 :param base_info: :return: """ # set uni-result output headers with open('../../utils/outputs.yaml', 'r') as f: out_headers = yaml.load(f, Loader=yaml.Loader)['algo_module_output'] result_to_classification = {} sns.set_theme(style="white", palette=None) switches = base_info['switches'] solarPV = LfpData(base_info['FileName'], base_info['paths'][0], base_info['paths'][1], base_info['BattMaker'], base_info['BattType']) vin, equipment_result_path, report_path, abnormal_path, stat_path = solarPV.initialization() temp, SOC, OCV, paths = solarPV.get_data(equipment_result_path, base_info['date_assign'][0], base_info['date_assign'][1]) cluster_name = temp['cluster'] print(f"\033[0;31;42m cluster {cluster_name} data imported. \033[0m") # 重点信息【数据已经导入完成】：红色字体绿色背景 # analysis on module data for m_esn in tqdm(temp['modules'].keys()): data_module = {'mod_esn': m_esn, 'data': temp['modules'][m_esn], 'n_volt_probe': temp['n_volt_probe'], 'n_temp_probe': temp['n_temp_probe']} print(f"module_id: {m_esn}, total rows: {len(data_module['data'])}") print(f'\033[0;31;42m module {m_esn} start process... \033[0m') # module全生命周期可视化 lifecycle_visual(m_esn, data_module, paths, switches) # 一致性算法模块 ica_analysis(m_esn, data_module, paths, base_info, out_headers) # 阈值告警算法 threshold_warning(m_esn, data_module, paths, base_info, out_headers) # 采样异常检测算法 # 内/外短路算法 # 故障分类分级算法 del data_module print(f"\033[0;31;42m Module {m_esn}: Cloud BMS Analysis completed. \033[0m") del temp return

可以考虑对代码进行如下优化：

1. 将函数内的注释改为函数外的文档字符串，方便查看函数说明。

2. 将一些常量、字符串放在函数外面作为全局变量，避免反复读取文件或字符串生成对象。

3. 使用 `if __name__ == '__main__'` 来保证模块被导入时不会执行代码。

4. 对于循环中的 `del` 操作，可以考虑使用 `with` 语句来自动关闭资源。

5. 可以将循环中的函数调用改为函数列表，使用循环来遍历调用，使代码更加简洁易读。

6. 建议使用 logging 模块来替代 print 函数，以便更好地管理日志。

修改后的代码如下：

import yaml
import logging
import sns
from tqdm import tqdm

from lifecycle_visual import lifecycle_visual
from ica_analysis import ica_analysis
from threshold_warning import threshold_warning
from lfp_data import LfpData


# 常量、字符串等全局变量
OUT_HEADERS = None


def load_out_headers():
    with open('../../utils/outputs.yaml', 'r') as f:
        return yaml.load(f, Loader=yaml.Loader)['algo_module_output']


def batch_analysis(base_info):
    """
    算法模块调用函数
    :param base_info:
    :return:
    """
    global OUT_HEADERS
    if OUT_HEADERS is None:
        OUT_HEADERS = load_out_headers()

    sns.set_theme(style="white", palette=None)

    switches = base_info['switches']
    solarPV = LfpData(base_info['FileName'], base_info['paths'][0], base_info['paths'][1],
                      base_info['BattMaker'], base_info['BattType'])
    vin, equipment_result_path, report_path, abnormal_path, stat_path = solarPV.initialization()
    temp, SOC, OCV, paths = solarPV.get_data(equipment_result_path, base_info['date_assign'][0],
                                             base_info['date_assign'][1])
    cluster_name = temp['cluster']
    logging.info(f"cluster {cluster_name} data imported.")

    # analysis on module data
    for m_esn in tqdm(temp['modules'].keys()):
        data_module = {'mod_esn': m_esn, 'data': temp['modules'][m_esn],
                       'n_volt_probe': temp['n_volt_probe'], 'n_temp_probe': temp['n_temp_probe']}
        logging.info(f"module_id: {m_esn}, total rows: {len(data_module['data'])}")
        logging.info(f'module {m_esn} start process...')

        # module全生命周期可视化
        lifecycle_visual(m_esn, data_module, paths, switches)

        # 一致性算法模块
        ica_analysis(m_esn, data_module, paths, base_info, OUT_HEADERS)

        # 阈值告警算法
        threshold_warning(m_esn, data_module, paths, base_info, OUT_HEADERS)

        # 采样异常检测算法
        # 内/外短路算法
        # 故障分类分级算法

        logging.info(f"Module {m_esn}: Cloud BMS Analysis completed.")

        # 删除临时数据，使用 with 语句自动关闭资源
        with data_module['data'] as f:
            del f

    with temp as f:
        del f

    return


if __name__ == '__main__':
    batch_analysis(base_info)