[ { "path": ".gitignore", "content": ".ipynb_checkpoints\n*.pyc\n.tox/\n*.egg-info\nmetro.json\npoc/\nnlp/*.txt\n.cache/\nbuild/\ndist/\n" }, { "path": ".travis.yml", "content": "sudo: false\nlanguage: python\nmatrix:\n include:\n - python: 3.4\n env: TOXENV=py34\n - python: 3.5\n env: TOXENV=py35\n - python: 3.6\n env: TOXENV=py36\n allow_failures:\n - python: 3.5\n env: TOXENV=py35\ninstall: pip install tox\ncache: pip\nscript: tox\nnotifications:\n email: false\n" }, { "path": "CODE_OF_CONDUCT.md", "content": "# Contributor Covenant Code of Conduct\n\n## Our Pledge\n\nIn the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, gender identity and expression, level of experience, nationality, personal appearance, race, religion, or sexual identity and orientation.\n\n## Our Standards\n\nExamples of behavior that contributes to creating a positive environment include:\n\n* Using welcoming and inclusive language\n* Being respectful of differing viewpoints and experiences\n* Gracefully accepting constructive criticism\n* Focusing on what is best for the community\n* Showing empathy towards other community members\n\nExamples of unacceptable behavior by participants include:\n\n* The use of sexualized language or imagery and unwelcome sexual attention or advances\n* Trolling, insulting/derogatory comments, and personal or political attacks\n* Public or private harassment\n* Publishing others' private information, such as a physical or electronic address, without explicit permission\n* Other conduct which could reasonably be considered inappropriate in a professional setting\n\n## Our Responsibilities\n\nProject maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.\n\nProject maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.\n\n## Scope\n\nThis Code of Conduct applies both within project spaces and in public spaces when an individual is representing the project or its community. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.\n\n## Enforcement\n\nInstances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting the project team at soros.liu1029@gmail.com. The project team will review and investigate all complaints, and will respond in a way that it deems appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.\n\nProject maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project's leadership.\n\n## Attribution\n\nThis Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4, available at [http://contributor-covenant.org/version/1/4][version]\n\n[homepage]: http://contributor-covenant.org\n[version]: http://contributor-covenant.org/version/1/4/\n" }, { "path": "LICENSE", "content": "MIT License\n\nCopyright (c) 2018 Soros Liu\n\nPermission is hereby granted, free of charge, to any person obtaining a copy\nof this software and associated documentation files (the \"Software\"), to deal\nin the Software without restriction, including without limitation the rights\nto use, copy, modify, merge, publish, distribute, sublicense, and/or sell\ncopies of the Software, and to permit persons to whom the Software is\nfurnished to do so, subject to the following conditions:\n\nThe above copyright notice and this permission notice shall be included in all\ncopies or substantial portions of the Software.\n\nTHE SOFTWARE IS PROVIDED \"AS IS\", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR\nIMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,\nFITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE\nAUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER\nLIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,\nOUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE\nSOFTWARE.\n" }, { "path": "MANIFEST.in", "content": "include README.rst LICENSE\nrecursive-include tests *.py" }, { "path": "README.rst", "content": "==================\nJike Metro 🚇\n==================\n\n.. image:: https://img.shields.io/travis/Sorosliu1029/Jike-Metro.svg\n :alt: Travis\n :target: https://travis-ci.org/Sorosliu1029/Jike-Metro\n\n.. image:: https://img.shields.io/pypi/v/jike.svg\n :alt: PyPI\n :target: https://pypi.org/project/jike/\n\n.. image:: https://img.shields.io/pypi/l/jike.svg\n :alt: PyPI - License\n :target: https://pypi.org/project/jike/\n\n.. image:: https://img.shields.io/pypi/pyversions/jike.svg\n :alt: PyPI - Python Version\n :target: https://pypi.org/project/jike/\n\n.. image:: https://img.shields.io/pypi/status/jike.svg\n :alt: PyPI - Status\n :target: https://pypi.org/project/jike/\n\n.. image:: https://img.shields.io/github/contributors/Sorosliu1029/Jike-Metro.svg\n :alt: GitHub contributors\n :target: https://github.com/Sorosliu1029/Jike-Metro/graphs/contributors\n\n.. image:: https://img.shields.io/badge/Say%20Thanks-!-1EAEDB.svg\n :alt: Say Thanks\n :target: https://saythanks.io/to/Sorosliu1029\n\n.. image:: https://img.shields.io/github/stars/Sorosliu1029/Jike-Metro.svg?style=social&label=Stars\n :alt: GitHub stars\n :target: https://github.com/Sorosliu1029/Jike-Metro/\n\nJike Metro 🚇 是即刻镇的地铁工程，旨在提高即友的出行游览效率。\n\n**安全提醒**：Jike Metro 🚇 目前是地下工作，非官方授权，随时可能翻车，给果果 🐈 买小鱼干可保平安。\n\n.. image:: https://cdn.jellow.site/Ftub2jUf092k6GYua0DTV8t-PMoR.jpg?imageView2/0/w/2000/h/400/q/50\n\n图片来源: `即刻九号工友“果果”和小伙伴们 `_\n\nJike Metro 🚇 简易乘车指南\n==========================\n\n.. code-block:: python\n\n >>> c = jike.JikeClient()\n >>> c.get_my_profile()\n User(id='58cf99696a34ae0015b9f5d5', screenName=挖地道的)\n >>> my_collection = c.get_my_collection()\n >>> my_collection[0]\n OfficialMessage(id='55dd572f41904d0e00fc58f8', content=即刻果果: 分享一只曾经的童星（已光速成长）)\n >>> news_feed = c.get_news_feed()\n >>> news_feed[0]\n OfficialMessage(id='5ac347a30799810017977041', content=DeepMind 发布新架构 让AI 边玩游戏边强化学习)\n >>> ceo = c.get_user_profile(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\n >>> ceo\n User(screenName=瓦恁)\n >>> c.create_my_post(content='Jike Metro 🚇 released!', link='https://github.com/Sorosliu1029/Jike-Metro')\n True\n\n更详细的乘车指南请移步 👉 `Jike Metro 🚇 乘车指南 `_\n\nJike Metro 🚇 乘车体验\n======================\n\nJike Metro 🚇 目前支持：\n\n- 获取自己的收藏，查看自己的用户信息\n- 流式获取首页消息和动态\n- 获取某个用户的用户信息、发布的动态、创建的主题、关注的主题、TA关注的人和关注TA的人\n- 获取某条消息/动态的评论\n- 获取某个主题下的精选和广场\n- 发布个人动态（可带图、带链接、带主题），删除个人动态\n- 点赞、收藏、评论、转发某条消息/动态\n- 在浏览器中打开某条消息的原始链接\n- 根据关键词搜索主题\n- 根据关键词搜索自己的收藏\n- 获取即刻首页的推荐关注主题列表（不限于首页显示的5个）\n\nJike Metro 🚇 现在支持 Python 3.4-3.6\n\nJike Metro 🚇 入口\n==================\n\n可通过 pip 安装 Jike Metro 🚇\n\n.. code-block:: bash\n\n $ pip install jike\n\n注意安全，小心行驶，不要影响其他即友的出行。\n\nJike Metro 🚇 基础设施\n======================\n\nJike Metro 🚇 基于：\n\n- `即刻Web版 `_\n- `requests `_\n- `qrcode `_\n" }, { "path": "docs/CNAME", "content": "jike-metro.sorosliu.xyz" }, { "path": "docs/convert.py", "content": "\"\"\"\nConvert Jupyter notebook to html\n\"\"\"\nimport os\nimport json\nfrom nbconvert import HTMLExporter\n\nINDEX_HTML_PATH = os.path.join('docs', 'index.html')\nTEMPLATE_PATH = os.path.join('docs', 'templates')\n\n\ndef gen_notebook_path():\n if not os.path.exists(INDEX_HTML_PATH):\n last_render_datetime = 0\n else:\n last_render_datetime = os.path.getmtime(INDEX_HTML_PATH)\n flt = lambda f: f.endswith('.ipynb') and 'checkpoint' not in f\n for dirpath, dirnames, filenames in os.walk(os.path.join('docs', 'source_notebooks')):\n for filename in filter(flt, filenames):\n source_ipynb_path = os.path.join(dirpath, filename)\n modified_datetime = os.path.getmtime(source_ipynb_path)\n if modified_datetime > last_render_datetime:\n yield source_ipynb_path\n\n\ndef arrange_notebook_execution_order(source_ipynb_path):\n with open(source_ipynb_path, 'rt', encoding='utf-8') as f:\n notebook = json.load(f)\n cnt = 1\n for cell in notebook['cells']:\n if cell['cell_type'] == 'code':\n cell['execution_count'] = cnt\n if cell['outputs']:\n assert len(cell['outputs']) == 1\n cell['outputs'][0]['execution_count'] = cnt\n cnt += 1\n\n with open(source_ipynb_path, 'wt', encoding='utf-8') as f:\n json.dump(notebook, f)\n\n\ndef convert():\n exporter = HTMLExporter()\n exporter.template_path = [os.path.join('docs', 'templates')]\n exporter.template_file = 'full'\n for source_ipynb_path in gen_notebook_path():\n arrange_notebook_execution_order(source_ipynb_path)\n _, filename = os.path.split(source_ipynb_path)\n\n body, _ = exporter.from_filename(source_ipynb_path)\n write_path = os.path.join('docs', filename.replace('ipynb', 'html'))\n with open(write_path, 'wt', encoding='utf-8') as f:\n f.write(body)\n print('{} write success.'.format(write_path))\n\n\ndef main():\n convert()\n\n\nif __name__ == '__main__':\n main()\n" }, { "path": "docs/example.html", "content": "\n\n\nexample\n\n\n\n\n\n\n\n\n\n\n
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简单的例子

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Prev: 乘车指南 🚇\nNext: Jike Metro 🚇 中各个类的可用属性

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In [1]:
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import jike\nc = jike.JikeClient()\n
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获取瓦总和不管姐粉丝性别百分比

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想法来自 劳斯判据 的 动态

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# 瓦总\nceo_follower = c.get_user_follower(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_follower\n
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ceo_follower.load_all()\n
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# 不管姐\nboss_follower = c.get_user_follower(username='B5C00109-15EA-4351-8B93-E58651E8C39D')\nboss_follower.load_all()\n
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ceo_male_fan_count = sum((follower.gender == 'MALE' for follower in ceo_follower))\nceo_female_fan_count = sum((follower.gender == 'FEMALE' for follower in ceo_follower))\nceo_other_fan_count = sum(follower.gender == None for follower in ceo_follower)\nprint(ceo_male_fan_count, ceo_female_fan_count, ceo_other_fan_count)\n
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12794 4070 3092\n
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boss_male_fan_count = sum((follower.gender == 'MALE' for follower in boss_follower))\nboss_female_fan_count = sum((follower.gender == 'FEMALE' for follower in boss_follower))\nboss_other_fan_count = sum(follower.gender == None for follower in boss_follower)\nprint(boss_male_fan_count, boss_female_fan_count, boss_other_fan_count)\n
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%matplotlib inline\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec\n\nlabels = 'male', 'female', 'unknown'\nceo_stats = (ceo_male_fan_count, ceo_female_fan_count, ceo_other_fan_count)\nboss_stats = (boss_male_fan_count, boss_female_fan_count, boss_other_fan_count)\n\nthe_grid = GridSpec(1, 2)\nplt.subplot(the_grid[0, 0], aspect=1)\nplt.title('ceo')\nplt.pie(ceo_stats, labels=labels, autopct='%1.1f%%', shadow=True)\nplt.subplot(the_grid[0, 1], aspect=1)\nplt.title('boss')\nplt.pie(boss_stats, labels=labels, autopct='%1.1f%%', shadow=True)\n
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店长发车的时间分布

基于最近一个月 不好笑便利店 主题下的精选，由评论判断是否开车

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In [8]:
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# 不好笑便利店 的主题精选\nselected = c.get_topic_selected(topic_id='5701d10d5002b912000e588d')\n
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from datetime import datetime, timedelta\ntoday = datetime.today()\na_month_ago = today - timedelta(days=30)\ndate_parse = lambda t: datetime.strptime(t[:-5], '%Y-%m-%dT%H:%M:%S')\n
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comment_keywords = {'奶', '任务', '爱尔兰', '不动产', '发车', '开车', '上车', '窑子', '黄色', '黄即', \n                    '片子', '看片', '借一部', '资源', '举报'}\n
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from collections import defaultdict\ntime_periods = defaultdict(int)\n
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message_date = today\nwhile message_date > a_month_ago:\n    messages = selected.load_more(limit=100)\n    for message in messages:\n        message_date = date_parse(message.createdAt)\n        comments = c.get_comment(message)\n        comments.load_full()\n        for comment in comments:\n            if any((keyword in comment.content for keyword in comment_keywords)):\n                time_periods[message_date.hour] += 1\n
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# UTC time, should +8 for Asia/Shanghai\nadjusted_time_periods = [((h+8)%24, time_periods[h]) for h in range(24)]\n
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adjusted_time_periods = adjusted_time_periods[-8:] + adjusted_time_periods[:-8]\nadjusted_time_periods\n
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[(0, 0),\n (1, 0),\n (2, 0),\n (3, 0),\n (4, 0),\n (5, 0),\n (6, 0),\n (7, 0),\n (8, 13),\n (9, 20),\n (10, 8),\n (11, 20),\n (12, 33),\n (13, 30),\n (14, 42),\n (15, 71),\n (16, 17),\n (17, 37),\n (18, 41),\n (19, 31),\n (20, 41),\n (21, 38),\n (22, 18),\n (23, 2)]
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店长最可能发车的三个时间段

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total_cnt = sum((cnt for _, cnt in adjusted_time_periods))\n\ndrive_time = sorted(adjusted_time_periods, key=lambda t: t[1], reverse=True)[:3]\nfor period, cnt in drive_time:\n    print('发车时间: {}点，发车概率: {:.2%}'.format(period, cnt / total_cnt))\n
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发车时间: 15点，发车概率: 15.37%\n发车时间: 14点，发车概率: 9.09%\n发车时间: 18点，发车概率: 8.87%\n
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店长发车的时间分布

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%matplotlib inline\nimport matplotlib.pyplot as plt\n\ndata = []\nfor h, cnt in adjusted_time_periods:\n    data.extend([h]*cnt)\nplt.hist(data, max(data)-min(data), facecolor='g', align='left', histtype='bar', rwidth=0.9)\nplt.xlabel('Hour of a day')\nplt.ylabel('Drive count')\nplt.title('Histogram of drive statistics')\nplt.grid(True)\nplt.show()\n
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生成⎡推荐关注⎦和⎡我关注的主题⎦对应关键词集的词云

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即刻Web端首页右侧栏的推荐关注

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recommended_topics = c.get_recommended_topic()\n
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加载至少200个推荐的主题

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while len(recommended_topics) < 200:\n    recommended_topics.load_more()\n
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recommended_topics\n
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给我推荐的前五个主题的关键词

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for topic in recommended_topics[:5]:\n    print(topic.keywords)\n
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单车 租赁 自行车 租赁 o2o ofo 骑行 mobike 共享单车\n美团 人人网 饭否 bat 马化腾 马云 李彦宏 张一鸣 大众点评 o2o 龙岩\n穿搭 搭配 服装 时尚\n网易云音乐 网易 网易云 云音乐 营销\n二次元 动漫 新番 动画 漫画 魔王 动漫头像\n
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我自己关注的主题

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my_subscribed_topics = c.get_user_subscribed_topic(username='WalleMax')\nmy_subscribed_topics.load_all()\n
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自己关注的最近五个主题的关键词

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for topic in my_subscribed_topics[:5]:\n    print(topic.keywords)\n
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想法 灵感 一个想法 杠精 思考\nNone\n彩虹 合唱 金承志 张志超 神曲 音乐 作品 合唱 古典 搞怪\n开箱 晒单 购物 评测\n追踪 抓取 内测 邀请码\n
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进行关键词计数

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In [23]:
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from collections import Counter\nrecommended_keywords_counter = Counter()\nsubscribed_keywords_counter = Counter()\n
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In [24]:
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for topic in recommended_topics:\n    recommended_keywords_counter.update(topic.keywords.split() if topic.keywords else [])\n
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“推荐关注”一共有1164个关键词

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In [25]:
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len(recommended_keywords_counter)\n
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Out[25]:
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1164
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“推荐关注”中出现频次最高的10个关键词及其对应频数

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In [26]:
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recommended_keywords_counter.most_common(10)\n
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Out[26]:
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[('搞笑', 14),\n ('电影', 12),\n ('明星', 10),\n ('综艺', 9),\n ('科技', 9),\n ('娱乐', 8),\n ('鹿晗', 7),\n ('新闻', 6),\n ('李易峰', 6),\n ('日本', 6)]
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In [27]:
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for topic in my_subscribed_topics:\n    subscribed_keywords_counter.update(topic.keywords.split() if topic.keywords else [])\n
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“我关注的主题”一共有953个关键词

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In [28]:
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len(subscribed_keywords_counter)\n
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Out[28]:
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953
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“我关注的主题”中出现频次最高的10个关键词及其对应频数

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In [29]:
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subscribed_keywords_counter.most_common(10)\n
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Out[29]:
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[('阅读', 13),\n ('搞笑', 12),\n ('科普', 11),\n ('热门', 11),\n ('恋爱', 10),\n ('好笑', 10),\n ('新闻', 10),\n ('科技', 9),\n ('哄妹子', 9),\n ('哄', 9)]
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好奇，怎么会有 “哄妹子” 呢？ 🤔🤔🤔

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基于 Word Cloud 生成关键词词云

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In [30]:
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from PIL import Image\nimport numpy as np\nimport matplotlib.pyplot as plt\nfrom matplotlib.gridspec import GridSpec\nfrom wordcloud import WordCloud\n
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In [31]:
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%matplotlib inline\nmask = np.array(Image.open('jike.png'))\nwc = WordCloud(background_color='white', max_words=100, mask=mask, width=1000, height=800,\n               relative_scaling=0.3, random_state=42,\n              font_path='/System/Library/Fonts/PingFang.ttc')\n\nthe_grid = GridSpec(1, 2)\nwc.generate_from_frequencies(recommended_keywords_counter)\nplt.subplot(the_grid[0, 0], aspect=1)\nplt.title('Recommendation World Cloud')\nplt.axis("off")\nplt.imshow(wc, interpolation="bilinear")\n\nwc.generate_from_frequencies(subscribed_keywords_counter)\nplt.subplot(the_grid[0, 1], aspect=1)\nplt.title('Subscription World Cloud')\nplt.axis("off")\nplt.imshow(wc, interpolation="bilinear")\n
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我觉得吧，“推荐关注”词云里那么大的“鹿晗”，“李易峰”，“综艺”，“明星”和我的个人气质不大符合啊 🤔

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即刻的推荐系统还有很大的进步空间

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由部分数据分析即友的分布

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获取 我就想定个位 主题下所有的广场动态

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In [32]:
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square = c.get_topic_square(topic_id='5aaf50b9127e30001759c57e')\n
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In [33]:
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from collections import defaultdict\ncity_counter = defaultdict(int)\nlocations = []\n
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记录动态的定位城市和定位经纬度

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In [34]:
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while square.load_more_key:\n    more = square.load_more()\n    for post in more:\n        if post.type == 'ORIGINAL_POST' and post.poi:\n            city_counter[post.poi['cityname']] += 1\n            locations.append(post.poi['location'])\n
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此主题广场下北京市的动态最多

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In [35]:
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max(city_counter.items(), key=lambda i: i[1])\n
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Out[35]:
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('北京市', 34)
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一共有406个经纬度信息

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In [36]:
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len(locations)\n
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Out[36]:
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406
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In [37]:
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from mpl_toolkits.basemap import Basemap\nimport matplotlib.pyplot as plt\n
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墨绿色的标记点即为动态的发布地点，可见：

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In [38]:
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%matplotlib inline\nplt.figure(figsize=(20,10))\nmap = Basemap(projection='merc', llcrnrlon=70, llcrnrlat=15,\n        urcrnrlon=140, urcrnrlat=55, lat_0=15, lon_0=95, resolution='h')\nmap.drawcoastlines(linewidth=0.25)\nmap.drawcountries(linewidth=0.25)\nmap.fillcontinents(color='coral',lake_color='aqua')\nmap.drawmapboundary(fill_color='aqua')\nfor loc in locations:\n    x, y = map(*loc)\n    map.plot(x, y, 'go', markersize=4)\nplt.title(' Jike POI Distribution ')\nplt.show()\n
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Prev: 乘车指南 🚇\nNext: Jike Metro 🚇 中各个类的可用属性

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乘车指南

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Prev: None\nNext: 简单的例子 🌰

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In [1]:
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import jike\n
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创建即刻客户端

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初次使用会要求使用即刻App扫描二维码登录

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登录成功之后，会在~/.local/jike/jike_metro.json存储即刻的auth_token，后续的使用则会跳过扫描二维码的步骤

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In [2]:
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c = jike.JikeClient()\n
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查看自己的用户信息

调用： get_my_profile()
\n返回： User，自己的用户信息 （基于collection.namedtuple）

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In [3]:
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my_profile = c.get_my_profile()\nmy_profile\n
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Out[3]:
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User(screenName=挖地道的)
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In [4]:
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print(my_profile.screenName)\nprint(my_profile.briefIntro)\n
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挖地道的\nⒿ 镇-地下工作者 👷\n
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获取自己的收藏

调用： get_my_collection()
\n返回： List，自己的收藏 （基于collection.abc.Sequence）

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In [5]:
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my_collection = c.get_my_collection()\nmy_collection\n
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Out[5]:
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List(20 items)
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In [6]:
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my_collection[0]\n
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Out[6]:
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OfficialMessage(id=55dd572f41904d0e00fc58f8, content=即刻果果: 分享一只曾经的童星（已光速成长）)
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In [7]:
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guoguo = my_collection[0]\nguoguo.likeCount\n
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Out[7]:
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39
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加载所有收藏

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In [8]:
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my_collection.load_all()\nmy_collection\n
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Out[8]:
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List(24 items)
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流式获取首页消息和动态

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  • 获取消息

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    调用： get_news_feed()
    \n 返回： Stream，消息流 （基于collection.deque）

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In [9]:
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news_feed = c.get_news_feed()\nnews_feed\n
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Out[9]:
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Stream(20 items, with 200 capacity)
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In [10]:
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news_feed[0]\n
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Out[10]:
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OfficialMessage(id=5ac392c18fecf20017ec27e7, content=姑姑住进了养老院)
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In [11]:
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news_feed.load_more()\nnews_feed\n
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Out[11]:
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Stream(40 items, with 200 capacity)
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  • 获取动态

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    调用： get_following_update()
    \n 返回： Stream，动态流 （基于collection.deque）

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In [12]:
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following_update = c.get_following_update()\nfollowing_update\n
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Out[12]:
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Stream(29 items, with 200 capacity)
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In [13]:
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following_update[0]\n
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Out[13]:
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OriginalPost(id=5ac392cd3535890017f8a3bb, content=所以「向拉斯维加斯学习」（1972）也可以是向东京学习。而且东京更早。#ATokyoMemoir #IanBuruma)
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In [14]:
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following_update.load_more()\nfollowing_update\n
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Out[14]:
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Stream(45 items, with 200 capacity)
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  • 获取当前未读消息数

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    调用：get_news_feed_unread_count()
    \n 返回：int，未读消息数

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In [15]:
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c.get_news_feed_unread_count()\n
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Out[15]:
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获取某个用户的用户信息、发布的动态、创建的主题、关注的主题、TA关注的人和关注TA的人

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  • 获取某个用户的用户信息

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    调用： get_user_profile(username)
    \n 参数： username: 指定用户的用户名（注：不是用户的显示名，而是类似 瓦总个人页面 在浏览器地址栏中 https://web.okjike.com/user/ 后的部分）
    \n 返回： User, 用户信息 （基于collection.namedtuple）

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In [16]:
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ceo = c.get_user_profile(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo\n
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Out[16]:
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User(screenName=瓦恁)
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In [17]:
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ceo.briefIntro\n
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Out[17]:
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'即刻CEO'
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  • 获取某个用户发布的动态

    \n

    调用： get_user_post(username)
    \n 参数： username: 指定用户的用户名
    \n 返回： List, 用户发布的动态 （基于collection.abc.Sequence）

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In [18]:
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ceo_posts = c.get_user_post(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_posts\n
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Out[18]:
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List(20 items)
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In [19]:
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ceo_posts[0]\n
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Out[19]:
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Repost(id=5ac36c4913fd9f0018a1a5ca, content=“战略是现实和理想的结合。伟大的战略是极端的现实主义和极端的理想主义结合的产物”)
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In [20]:
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ceo_posts.load_more()\nceo_posts\n
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Out[20]:
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List(59 items)
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load_all 约运行了10s

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In [21]:
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ceo_posts.load_all()\nceo_posts\n
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Out[21]:
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List(3011 items)
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瓦总的第一条动态

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In [22]:
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ceo_posts[-1]\n
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Out[22]:
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Repost(id=5ab20efc63cd65165515d4e2, content=想给这个主题打钱)
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In [23]:
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ceo_posts[-1].createdAt\n
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Out[23]:
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'2016-09-14T11:30:58.283Z'
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  • 获取某个用户创建的主题

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    调用： get_user_created_topic(username)
    \n 参数： username: 指定用户的用户名
    \n 返回： List, 用户创建的主题 （基于collection.abc.Sequence）

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In [24]:
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ceo_created_topics = c.get_user_created_topic(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_created_topics[0]\n
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Out[24]:
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Topic(id=5a8eeb3d4eb3b0001858fa87, content=又有人在微博提到yes prime minister了)
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  • 获取某个用户关注的主题

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    调用： get_user_subscribed_topic(username)
    \n 参数： username: 指定用户的用户名
    \n 返回： List, 用户关注的主题 （基于collection.abc.Sequence）

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In [25]:
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ceo_subscribed_topics = c.get_user_subscribed_topic(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_subscribed_topics[0]\n
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Out[25]:
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Topic(id=5a41c69600074100168fd2a1, content=又有人在微博提到Reddit)
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  • 获取某个用户关注的人

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    调用： get_user_following(username)
    \n 参数： username: 指定用户的用户名
    \n 返回： List, 用户关注的人 （基于collection.abc.Sequence）

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In [26]:
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ceo_followings = c.get_user_following(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_followings[0]\n
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Out[26]:
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User(screenName=谌谌)
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  • 获取关注某个用户的人

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    调用： get_user_follower(username)
    \n 参数： username: 指定用户的用户名
    \n 返回： List, 关注指定用户的人 （基于collection.abc.Sequence）

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In [27]:
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ceo_followers = c.get_user_follower(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\nceo_followers[0]\n
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Out[27]:
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User(screenName=夏洛克牌花生酱)
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获取某个主题下的精选和广场

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  • 获取某个主题下的精选

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    调用： get_topic_selected(topic_id)
    \n 参数： topic_id: 指定主题的id，类似于 不好笑便利店 地址栏部分在 https://web.okjike.com/topic/之后的部分
    \n 返回： Stream, 主题精选（基于collection.deque）

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In [28]:
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topic_selected = c.get_topic_selected(topic_id='5701d10d5002b912000e588d')\ntopic_selected[0]\n
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Out[28]:
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OfficialMessage(id=5ac371e0a7476600171f1a31, content=干死美团 ，碾压滴滴！饿了么和你一起拼)
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  • 获取某个主题下的广场

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    调用： get_topic_square(topic_id)
    \n 参数： topic_id: 指定主题的id
    \n 返回： Stream, 主题广场（基于collection.deque）

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In [29]:
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topic_square = c.get_topic_square(topic_id='5701d10d5002b912000e588d')\ntopic_square[0]\n
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Out[29]:
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OriginalPost(id=5a43395fc912390015ec6b6a, content=不正经，不上纲上线，偶尔开车，当然也欢迎你来代驾。)
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获取某条消息/动态的评论

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调用： get_comment(message)
\n参数： message: 要获取评论的消息/动态
\n返回： Stream, 评论 （基于collection.deque）

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In [30]:
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comments = c.get_comment(topic_square[0])\ncomments\n
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Out[30]:
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Stream(13 items, with 200 capacity)
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In [31]:
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max(comments, key=lambda c: c.likeCount)\n
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Out[31]:
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Comment(id=5a4341eb57b9c60010c707cb, content=神特么偶尔。不是在开车就是在找车吧)
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在浏览器中打开某条消息的原始链接

调用：open_in_browser(url_or_message)
\n参数：url_or_message: url 或者 message (例如topic_selected[0]，这是一条OfficialMessage)
\n返回：None

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In [32]:
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c.open_in_browser(topic_selected[0])\n
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发布个人动态（可带图、带链接、带主题）

调用： create_my_post(content, link, topic_id, pictures)
\n参数： content: 要发布的内容 / link: 所带的链接 / topic_id: 所带主题的id / pictures: 所带图的本地地址（注：由于即刻的限制，动态不能同时带有图片和链接）
\n返回： OriginalPost，所发布的动态 （基于collection.namedtuple）

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In [33]:
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my_new_post = c.create_my_post('Hello world from Jike Metro 🚇!')\nmy_new_post\n
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Out[33]:
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OriginalPost(id=5ac3990c2391fb00174e3843, content=Hello world from Jike Metro 🚇!)
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点赞、收藏、评论、转发某条消息/动态

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  • 点赞某条消息/动态

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    调用： like_it(message)
    \n 参数： message: 要赞的消息/动态
    \n 返回： Bool, 成功与否

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In [34]:
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c.like_it(my_new_post)\n
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Out[34]:
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True
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  • 取消赞某条消息/动态

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    调用： unlike_it(message)
    \n 参数： message: 要取消赞的消息/动态
    \n 返回： Bool, 成功与否

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In [35]:
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c.unlike_it(my_new_post)\n
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Out[35]:
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True
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  • 收藏某条消息/动态

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    调用： collect_it(message)
    \n 参数： message: 要收藏的消息/动态
    \n 返回： Bool, 成功与否

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In [36]:
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c.collect_it(my_new_post)\n
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Out[36]:
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True
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  • 取消收藏某条消息/动态

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    调用： uncollect_it(message)
    \n 参数： message: 要取消收藏的消息/动态
    \n 返回： Bool, 成功与否

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In [37]:
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c.uncollect_it(my_new_post)\n
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Out[37]:
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True
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  • 转发某条消息/动态

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    调用： repost_it(content, message, sync_comment)
    \n 参数： content: 转发的评论内容 / message: 要转发的消息或动态 / sync_comment: 是否同步评论，默认为True
    \n 返回： Repost, 发布的转发 （基于collection.namedtuple)

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In [38]:
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c.repost_it('Support Jike Metro 🚇', my_new_post)\n
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Out[38]:
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Repost(id=5ac399fd44f1020018533abc, content=Support Jike Metro 🚇)
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  • 评论某条消息/动态

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    调用： comment_it(content, message, pictures, sync2personal_updates)
    \n 参数： content: 要评论内容 / message: 要评论的消息或动态 / pictures: 所带图的本地地址 / sync2personal_updates: 是否同步到个人动态，默认为True
    \n 返回： Comment, 发布的评论 （基于collection.namedtuple)

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In [39]:
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c.comment_it('Upvote for Jike Metro 🚇', my_new_post)\n
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Out[39]:
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Comment(id=5ac39a364ce4cf001702d51a, content=Upvote for Jike Metro 🚇)
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删除个人动态

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调用： delete_my_post(post)
\n参数： post: 要删除的动态
\n返回： Bool, 成功与否

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In [40]:
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c.delete_my_post(my_new_post)\n
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Out[40]:
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根据关键词搜索主题

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调用： search_topic(keywords)
\n参数： keywords：搜索的关键词
\n返回： List， 搜索到的主题， （基于collection.abc.Sequence)

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In [41]:
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topics = c.search_topic(keywords='apple')\ntopics\n
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Out[41]:
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List(20 items)
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In [42]:
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[topic['content'] for topic in topics]\n
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Out[42]:
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['Apple Watch',\n 'Apple Pay新动向',\n '下一代iPhone最新情报',\n '内地有新的Apple Store开业',\n '苹果有新的官方视频',\n '苹果特惠日提醒',\n '每天一个Mac快捷键介绍',\n 'Mac硬件新品及系统更新追踪',\n 'Mac Stories博客“Stories”板块新文章',\n 'Apple照片更新提醒',\n 'Apple Newsroom 更新',\n 'apple产品优惠',\n 'Apple购买提醒',\n 'Apple的东西有更新',\n 'Apple 照片 更新提醒',\n 'Apple Music照片更新提醒',\n 'Apple Store 有新的官翻产品上架',\n '有值得买的APPLE苹果优惠',\n '有值得买的Apple watch优惠',\n 'Apple 广告有新的配乐']
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Prev: None\nNext: 简单的例子 🌰

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即刻Web API

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Prev: Jike Metro 🚇 中各个类的可用属性\nNext: None

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In [1]:
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from jike.constants import ENDPOINTS\n
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In [2]:
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for k, v in ENDPOINTS.items():\n    if '{t}' not in v:\n        print('Function: {fn}, URL: {url}\\n'.format(fn=k, url=v))\n
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Function: create_session, URL: https://app.jike.ruguoapp.com/sessions.create\n\nFunction: wait_login, URL: https://app.jike.ruguoapp.com/sessions.wait_for_login\n\nFunction: confirm_login, URL: https://app.jike.ruguoapp.com/sessions.wait_for_confirmation\n\nFunction: my_collections, URL: https://app.jike.ruguoapp.com/1.0/users/collections/list\n\nFunction: news_feed, URL: https://app.jike.ruguoapp.com/1.0/newsFeed/list\n\nFunction: news_feed_unread_count, URL: https://app.jike.ruguoapp.com//1.0/newsFeed/countUnreads\n\nFunction: following_update, URL: https://app.jike.ruguoapp.com/1.0/personalUpdate/followingUpdates\n\nFunction: user_profile, URL: https://app.jike.ruguoapp.com/1.0/users/profile\n\nFunction: user_post, URL: https://app.jike.ruguoapp.com/1.0/personalUpdate/single\n\nFunction: user_created_topic, URL: https://app.jike.ruguoapp.com/1.0/customTopics/custom/listCreated\n\nFunction: user_subscribed_topic, URL: https://app.jike.ruguoapp.com/1.0/users/topics/listSubscribed\n\nFunction: user_following, URL: https://app.jike.ruguoapp.com/1.0/userRelation/getFollowingList\n\nFunction: user_follower, URL: https://app.jike.ruguoapp.com/1.0/userRelation/getFollowerList\n\nFunction: topic_selected, URL: https://app.jike.ruguoapp.com/1.0/messages/history\n\nFunction: topic_square, URL: https://app.jike.ruguoapp.com/1.0/squarePosts/list\n\nFunction: list_comment, URL: https://app.jike.ruguoapp.com/1.0/comments/listPrimary\n\nFunction: create_post, URL: https://app.jike.ruguoapp.com/1.0/originalPosts/create\n\nFunction: delete_post, URL: https://app.jike.ruguoapp.com/1.0/originalPosts/remove\n\nFunction: extract_link, URL: https://app.jike.ruguoapp.com/1.0/readability/extract\n\nFunction: picture_uptoken, URL: https://upload.jike.ruguoapp.com/token\n\nFunction: picture_upload, URL: https://up.qbox.me/\n\nFunction: repost_it, URL: https://app.jike.ruguoapp.com/1.0/reposts/add\n\nFunction: comment_it, URL: https://app.jike.ruguoapp.com/1.0/comments/add\n\nFunction: search_topic, URL: https://app.jike.ruguoapp.com/1.0/users/topics/search\n\n
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以下的API调用需要对应特定的消息类型

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假设消息类型为'ORIGINAL_MESSAGE', 则对应的URL部分为'originalMessages'

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message_type = 'originalMessages'\n
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for k, v in ENDPOINTS.items():\n    if '{t}' in v:\n        v = v.format(t=message_type)\n        print('Function: {fn}, URL: {url}\\n'.format(fn=k, url=v))\n
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Function: like_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/like\n\nFunction: unlike_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/unlike\n\nFunction: collect_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/collect\n\nFunction: uncollect_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/uncollect\n\n
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各个URL具体的HTTP BODY在这里就不赘述了，感兴趣的可以在浏览器调试器里追踪查看API调用的 json payload 和 json response

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Prev: Jike Metro 🚇 中各个类的可用属性\nNext: None

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Jike Metro 🚇 中各个类的可用属性

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Prev: 简单的例子 🌰\nNext: 即刻Web API

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In [1]:
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from jike.objects import *\n
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User 类可用的属性

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In [2]:
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for i in range(len(User._fields) // 5 + 1):\n    print(User._fields[i*5:(i+1)*5])\n
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('areaCode', 'avatarImage', 'backgroundImage', 'bio', 'birthday')\n('briefIntro', 'city', 'country', 'following', 'gender')\n('id', 'initUsername', 'isBetaUser', 'isLoginUser', 'isVerified')\n('mobilePhoneNumber', 'preferences', 'province', 'profileImageUrl', 'ref')\n('qqOpenId', 'screenName', 'updatedAt', 'userId', 'username')\n('usernameSet', 'verifyMessage', 'wechatOpenId', 'weiboUid', 'weiboUserInfo')\n('followedCount', 'followingCount', 'highlightedPersonalUpdates', 'liked', 'topicCreated')\n('topicSubscribed',)\n
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Topic 类可用的属性

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In [3]:
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for i in range(len(Topic._fields) // 5 + 1):\n    print(Topic._fields[i*5:(i+1)*5])\n
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('briefIntro', 'content', 'createdAt', 'enableForUserPost', 'enablePictureComments')\n('friendsAlsoSubscribe', 'id', 'isAnonymous', 'isDreamTopic', 'isValid')\n('keywords', 'lastMessagePostTime', 'likeIcon', 'maintainer', 'messagePrefix')\n('newCategory', 'operateStatus', 'pictureUrl', 'rectanglePicture', 'ref')\n('squarePicture', 'subscribedAt', 'subscribedStatusRawValue', 'subscribersCount', 'thumbnailUrl')\n('timeForRank', 'topicId', 'topicType', 'updatedAt')\n
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OfficialMessage 类可用的属性

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In [4]:
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for i in range(len(OfficialMessage._fields) // 5 + 1):\n    print(OfficialMessage._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\n('pictures', 'liked', 'likeIcon', 'collected', 'id')\n('targetType', 'status', 'viewType', 'createdAt', 'target')\n('likeCount', 'video', 'collectTime', 'abstract', 'content')\n()\n
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OriginalPost 类可用的属性

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In [5]:
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for i in range(len(OriginalPost._fields) // 5 + 1):\n    print(OriginalPost._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'poi', 'commentCount', 'read', 'messageId')\n('isCommentForbidden', 'type', 'pictures', 'liked', 'likeIcon')\n('collected', 'id', 'targetType', 'status', 'viewType')\n('createdAt', 'target', 'likeCount', 'collectTime', 'content')\n()\n
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Repost 类可用的属性

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In [6]:
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for i in range(len(Repost._fields) // 5 + 1):\n    print(Repost._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\n('pictures', 'liked', 'likeIcon', 'collected', 'id')\n('syncCommentId', 'targetType', 'status', 'replyToComment', 'viewType')\n('createdAt', 'target', 'likeCount', 'collectTime', 'content')\n()\n
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Question 类可用的属性

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In [7]:
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for i in range(len(Question._fields) // 5 + 1):\n    print(Question._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\n('pictures', 'liked', 'likeIcon', 'collected', 'updatedAt')\n('id', 'targetType', 'status', 'answerCount', 'viewType')\n('createdAt', 'target', 'likeCount', 'collectTime', 'title')\n('content',)\n
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Answer 类可用的属性

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In [8]:
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for i in range(len(Answer._fields) // 5 + 1):\n    print(Answer._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'richtextContent', 'commentCount', 'read', 'questionId')\n('isCommentForbidden', 'type', 'pictures', 'liked', 'voteTend')\n('likeIcon', 'collected', 'id', 'targetType', 'status')\n('upVoteCount', 'viewType', 'createdAt', 'target', 'likeCount')\n('collectTime', 'question', 'content')\n
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Comment 类可用的属性

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In [9]:
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for i in range(len(Comment._fields) // 5 + 1):\n    print(Comment._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'replyCount', 'collectedTime')\n('likeInfo', 'topic', 'level', 'commentCount', 'enablePictureComments')\n('read', 'threadId', 'isCommentForbidden', 'type', 'pictures')\n('liked', 'hotReplies', 'targetId', 'likeIcon', 'collected')\n('id', 'targetType', 'status', 'viewType', 'createdAt')\n('target', 'likeCount', 'collectTime', 'content')\n
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PersonalUpdateSection 类可用的属性

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In [10]:
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for i in range(len(PersonalUpdateSection._fields) // 5 + 1):\n    print(PersonalUpdateSection._fields[i*5:(i+1)*5])\n
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('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\n('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\n('pictures', 'liked', 'items', 'likeIcon', 'collected')\n('id', 'targetType', 'status', 'viewType', 'createdAt')\n('target', 'likeCount', 'collectTime', 'content')\n
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PersonalUpdate 类可用的属性

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In [11]:
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for i in range(len(PersonalUpdate._fields) // 5 + 1):\n    print(PersonalUpdate._fields[i*5:(i+1)*5])\n
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('linkInfo', 'topics', 'repostCount', 'user', 'targetUsers')\n('collectedTime', 'likeInfo', 'topic', 'topicIds', 'usernames')\n('commentCount', 'read', 'actionTime', 'isCommentForbidden', 'type')\n('pictures', 'liked', 'users', 'likeIcon', 'collected')\n('targetUsernames', 'action', 'id', 'targetType', 'status')\n('viewType', 'createdAt', 'target', 'likeCount', 'collectTime')\n('updateIds', 'content')\n
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Prev: 简单的例子 🌰\nNext: 即刻Web API

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\n\n\n \n\n\n\n" }, { "path": "docs/source_notebooks/example.ipynb", "content": "{\"cells\": [{\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"# \\u7b80\\u5355\\u7684\\u4f8b\\u5b50\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"Prev: \\u4e58\\u8f66\\u6307\\u5357 \\ud83d\\ude87\\n\", \"Next: Jike Metro \\ud83d\\ude87 \\u4e2d\\u5404\\u4e2a\\u7c7b\\u7684\\u53ef\\u7528\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 1, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"import jike\\n\", \"c = jike.JikeClient()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u83b7\\u53d6\\u74e6\\u603b\\u548c\\u4e0d\\u7ba1\\u59d0\\u7c89\\u4e1d\\u6027\\u522b\\u767e\\u5206\\u6bd4\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u60f3\\u6cd5\\u6765\\u81ea [\\u52b3\\u65af\\u5224\\u636e](https://web.okjike.com/user/c7d257c7-a4fc-4383-a779-49f3123adfab/post) \\u7684 [\\u52a8\\u6001](https://web.okjike.com/post-detail/5ac0a1dee72e500017c5e47f/originalPost)\"]}, {\"cell_type\": \"code\", \"execution_count\": 2, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(20 items)\"]}, \"execution_count\": 2, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"# \\u74e6\\u603b\\n\", \"ceo_follower = c.get_user_follower(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_follower\"]}, {\"cell_type\": \"code\", \"execution_count\": 3, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"19956\"]}, \"execution_count\": 3, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_follower.load_all()\"]}, {\"cell_type\": \"code\", \"execution_count\": 4, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"4388\"]}, \"execution_count\": 4, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"# \\u4e0d\\u7ba1\\u59d0\\n\", \"boss_follower = c.get_user_follower(username='B5C00109-15EA-4351-8B93-E58651E8C39D')\\n\", \"boss_follower.load_all()\"]}, {\"cell_type\": \"code\", \"execution_count\": 5, \"metadata\": {}, \"outputs\": [{\"execution_count\": 5, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"12794 4070 3092\\n\"]}], \"source\": [\"ceo_male_fan_count = sum((follower.gender == 'MALE' for follower in ceo_follower))\\n\", \"ceo_female_fan_count = sum((follower.gender == 'FEMALE' for follower in ceo_follower))\\n\", \"ceo_other_fan_count = sum(follower.gender == None for follower in ceo_follower)\\n\", \"print(ceo_male_fan_count, ceo_female_fan_count, ceo_other_fan_count)\"]}, {\"cell_type\": \"code\", \"execution_count\": 6, \"metadata\": {}, \"outputs\": [{\"execution_count\": 6, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"2844 1163 381\\n\"]}], \"source\": [\"boss_male_fan_count = sum((follower.gender == 'MALE' for follower in boss_follower))\\n\", \"boss_female_fan_count = sum((follower.gender == 'FEMALE' for follower in boss_follower))\\n\", \"boss_other_fan_count = sum(follower.gender == None for follower in boss_follower)\\n\", \"print(boss_male_fan_count, boss_female_fan_count, boss_other_fan_count)\"]}, {\"cell_type\": \"code\", \"execution_count\": 7, \"metadata\": {}, \"outputs\": [{\"data\": {\"image/png\": 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\\n\", \"text/plain\": [\"
\"]}, \"execution_count\": 7, \"metadata\": {}, \"output_type\": \"display_data\"}], \"source\": [\"%matplotlib inline\\n\", \"import matplotlib.pyplot as plt\\n\", \"from matplotlib.gridspec import GridSpec\\n\", \"\\n\", \"labels = 'male', 'female', 'unknown'\\n\", \"ceo_stats = (ceo_male_fan_count, ceo_female_fan_count, ceo_other_fan_count)\\n\", \"boss_stats = (boss_male_fan_count, boss_female_fan_count, boss_other_fan_count)\\n\", \"\\n\", \"the_grid = GridSpec(1, 2)\\n\", \"plt.subplot(the_grid[0, 0], aspect=1)\\n\", \"plt.title('ceo')\\n\", \"plt.pie(ceo_stats, labels=labels, autopct='%1.1f%%', shadow=True)\\n\", \"plt.subplot(the_grid[0, 1], aspect=1)\\n\", \"plt.title('boss')\\n\", \"plt.pie(boss_stats, labels=labels, autopct='%1.1f%%', shadow=True)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u5e97\\u957f\\u53d1\\u8f66\\u7684\\u65f6\\u95f4\\u5206\\u5e03\\n\", \"\\n\", \"\\u57fa\\u4e8e\\u6700\\u8fd1\\u4e00\\u4e2a\\u6708 [\\u4e0d\\u597d\\u7b11\\u4fbf\\u5229\\u5e97](https://web.okjike.com/topic/5701d10d5002b912000e588d/official) \\u4e3b\\u9898\\u4e0b\\u7684\\u7cbe\\u9009\\uff0c\\u7531\\u8bc4\\u8bba\\u5224\\u65ad\\u662f\\u5426\\u5f00\\u8f66\"]}, {\"cell_type\": \"code\", \"execution_count\": 8, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"# \\u4e0d\\u597d\\u7b11\\u4fbf\\u5229\\u5e97 \\u7684\\u4e3b\\u9898\\u7cbe\\u9009\\n\", \"selected = c.get_topic_selected(topic_id='5701d10d5002b912000e588d')\"]}, {\"cell_type\": \"code\", \"execution_count\": 9, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"from datetime import datetime, timedelta\\n\", \"today = datetime.today()\\n\", \"a_month_ago = today - timedelta(days=30)\\n\", \"date_parse = lambda t: datetime.strptime(t[:-5], '%Y-%m-%dT%H:%M:%S')\"]}, {\"cell_type\": \"code\", \"execution_count\": 10, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"comment_keywords = {'\\u5976', '\\u4efb\\u52a1', '\\u7231\\u5c14\\u5170', '\\u4e0d\\u52a8\\u4ea7', '\\u53d1\\u8f66', '\\u5f00\\u8f66', '\\u4e0a\\u8f66', '\\u7a91\\u5b50', '\\u9ec4\\u8272', '\\u9ec4\\u5373', \\n\", \" '\\u7247\\u5b50', '\\u770b\\u7247', '\\u501f\\u4e00\\u90e8', '\\u8d44\\u6e90', '\\u4e3e\\u62a5'}\"]}, {\"cell_type\": \"code\", \"execution_count\": 11, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"from collections import defaultdict\\n\", \"time_periods = defaultdict(int)\"]}, {\"cell_type\": \"code\", \"execution_count\": 12, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"message_date = today\\n\", \"while message_date > a_month_ago:\\n\", \" messages = selected.load_more(limit=100)\\n\", \" for message in messages:\\n\", \" message_date = date_parse(message.createdAt)\\n\", \" comments = c.get_comment(message)\\n\", \" comments.load_full()\\n\", \" for comment in comments:\\n\", \" if any((keyword in comment.content for keyword in comment_keywords)):\\n\", \" time_periods[message_date.hour] += 1\"]}, {\"cell_type\": \"code\", \"execution_count\": 13, \"metadata\": {\"collapsed\": true}, \"outputs\": [], \"source\": [\"# UTC time, should +8 for Asia/Shanghai\\n\", \"adjusted_time_periods = [((h+8)%24, time_periods[h]) for h in range(24)]\"]}, {\"cell_type\": \"code\", \"execution_count\": 14, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"[(0, 0),\\n\", \" (1, 0),\\n\", \" (2, 0),\\n\", \" (3, 0),\\n\", \" (4, 0),\\n\", \" (5, 0),\\n\", \" (6, 0),\\n\", \" (7, 0),\\n\", \" (8, 13),\\n\", \" (9, 20),\\n\", \" (10, 8),\\n\", \" (11, 20),\\n\", \" (12, 33),\\n\", \" (13, 30),\\n\", \" (14, 42),\\n\", \" (15, 71),\\n\", \" (16, 17),\\n\", \" (17, 37),\\n\", \" (18, 41),\\n\", \" (19, 31),\\n\", \" (20, 41),\\n\", \" (21, 38),\\n\", \" (22, 18),\\n\", \" (23, 2)]\"]}, \"execution_count\": 14, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"adjusted_time_periods = adjusted_time_periods[-8:] + adjusted_time_periods[:-8]\\n\", \"adjusted_time_periods\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u5e97\\u957f\\u6700\\u53ef\\u80fd\\u53d1\\u8f66\\u7684\\u4e09\\u4e2a\\u65f6\\u95f4\\u6bb5\"]}, {\"cell_type\": \"code\", \"execution_count\": 15, \"metadata\": {}, \"outputs\": [{\"execution_count\": 15, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"\\u53d1\\u8f66\\u65f6\\u95f4: 15\\u70b9\\uff0c\\u53d1\\u8f66\\u6982\\u7387: 15.37%\\n\", \"\\u53d1\\u8f66\\u65f6\\u95f4: 14\\u70b9\\uff0c\\u53d1\\u8f66\\u6982\\u7387: 9.09%\\n\", \"\\u53d1\\u8f66\\u65f6\\u95f4: 18\\u70b9\\uff0c\\u53d1\\u8f66\\u6982\\u7387: 8.87%\\n\"]}], \"source\": [\"total_cnt = sum((cnt for _, cnt in adjusted_time_periods))\\n\", \"\\n\", \"drive_time = sorted(adjusted_time_periods, key=lambda t: t[1], reverse=True)[:3]\\n\", \"for period, cnt in drive_time:\\n\", \" print('\\u53d1\\u8f66\\u65f6\\u95f4: {}\\u70b9\\uff0c\\u53d1\\u8f66\\u6982\\u7387: {:.2%}'.format(period, cnt / total_cnt))\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u5e97\\u957f\\u53d1\\u8f66\\u7684\\u65f6\\u95f4\\u5206\\u5e03\"]}, {\"cell_type\": \"code\", \"execution_count\": 16, \"metadata\": {}, \"outputs\": [{\"data\": {\"image/png\": \"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\\n\", \"text/plain\": [\"
\"]}, \"execution_count\": 16, \"metadata\": {}, \"output_type\": \"display_data\"}], \"source\": [\"%matplotlib inline\\n\", \"import matplotlib.pyplot as plt\\n\", \"\\n\", \"data = []\\n\", \"for h, cnt in adjusted_time_periods:\\n\", \" data.extend([h]*cnt)\\n\", \"plt.hist(data, max(data)-min(data), facecolor='g', align='left', histtype='bar', rwidth=0.9)\\n\", \"plt.xlabel('Hour of a day')\\n\", \"plt.ylabel('Drive count')\\n\", \"plt.title('Histogram of drive statistics')\\n\", \"plt.grid(True)\\n\", \"plt.show()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u751f\\u6210\\u23a1\\u63a8\\u8350\\u5173\\u6ce8\\u23a6\\u548c\\u23a1\\u6211\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\\u23a6\\u5bf9\\u5e94\\u5173\\u952e\\u8bcd\\u96c6\\u7684\\u8bcd\\u4e91\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u5373\\u523bWeb\\u7aef\\u9996\\u9875\\u53f3\\u4fa7\\u680f\\u7684\\u63a8\\u8350\\u5173\\u6ce8\"]}, {\"cell_type\": \"code\", \"execution_count\": 17, \"metadata\": {}, \"outputs\": [], \"source\": [\"recommended_topics = c.get_recommended_topic()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u52a0\\u8f7d\\u81f3\\u5c11200\\u4e2a\\u63a8\\u8350\\u7684\\u4e3b\\u9898\"]}, {\"cell_type\": \"code\", \"execution_count\": 18, \"metadata\": {}, \"outputs\": [], \"source\": [\"while len(recommended_topics) < 200:\\n\", \" recommended_topics.load_more()\"]}, {\"cell_type\": \"code\", \"execution_count\": 19, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(217 items)\"]}, \"execution_count\": 19, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"recommended_topics\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u7ed9\\u6211\\u63a8\\u8350\\u7684\\u524d\\u4e94\\u4e2a\\u4e3b\\u9898\\u7684\\u5173\\u952e\\u8bcd\"]}, {\"cell_type\": \"code\", \"execution_count\": 20, \"metadata\": {}, \"outputs\": [{\"execution_count\": 20, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"\\u5355\\u8f66 \\u79df\\u8d41 \\u81ea\\u884c\\u8f66 \\u79df\\u8d41 o2o ofo \\u9a91\\u884c mobike \\u5171\\u4eab\\u5355\\u8f66\\n\", \"\\u7f8e\\u56e2 \\u4eba\\u4eba\\u7f51 \\u996d\\u5426 bat \\u9a6c\\u5316\\u817e \\u9a6c\\u4e91 \\u674e\\u5f66\\u5b8f \\u5f20\\u4e00\\u9e23 \\u5927\\u4f17\\u70b9\\u8bc4 o2o \\u9f99\\u5ca9\\n\", \"\\u7a7f\\u642d \\u642d\\u914d \\u670d\\u88c5 \\u65f6\\u5c1a\\n\", \"\\u7f51\\u6613\\u4e91\\u97f3\\u4e50 \\u7f51\\u6613 \\u7f51\\u6613\\u4e91 \\u4e91\\u97f3\\u4e50 \\u8425\\u9500\\n\", \"\\u4e8c\\u6b21\\u5143 \\u52a8\\u6f2b \\u65b0\\u756a \\u52a8\\u753b \\u6f2b\\u753b \\u9b54\\u738b \\u52a8\\u6f2b\\u5934\\u50cf\\n\"]}], \"source\": [\"for topic in recommended_topics[:5]:\\n\", \" print(topic.keywords)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u6211\\u81ea\\u5df1\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\"]}, {\"cell_type\": \"code\", \"execution_count\": 21, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"167\"]}, \"execution_count\": 21, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_subscribed_topics = c.get_user_subscribed_topic(username='WalleMax')\\n\", \"my_subscribed_topics.load_all()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u81ea\\u5df1\\u5173\\u6ce8\\u7684\\u6700\\u8fd1\\u4e94\\u4e2a\\u4e3b\\u9898\\u7684\\u5173\\u952e\\u8bcd\"]}, {\"cell_type\": \"code\", \"execution_count\": 22, \"metadata\": {}, \"outputs\": [{\"execution_count\": 22, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"\\u60f3\\u6cd5 \\u7075\\u611f \\u4e00\\u4e2a\\u60f3\\u6cd5 \\u6760\\u7cbe \\u601d\\u8003\\n\", \"None\\n\", \"\\u5f69\\u8679 \\u5408\\u5531 \\u91d1\\u627f\\u5fd7 \\u5f20\\u5fd7\\u8d85 \\u795e\\u66f2 \\u97f3\\u4e50 \\u4f5c\\u54c1 \\u5408\\u5531 \\u53e4\\u5178 \\u641e\\u602a\\n\", \"\\u5f00\\u7bb1 \\u6652\\u5355 \\u8d2d\\u7269 \\u8bc4\\u6d4b\\n\", \"\\u8ffd\\u8e2a \\u6293\\u53d6 \\u5185\\u6d4b \\u9080\\u8bf7\\u7801\\n\"]}], \"source\": [\"for topic in my_subscribed_topics[:5]:\\n\", \" print(topic.keywords)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u8fdb\\u884c\\u5173\\u952e\\u8bcd\\u8ba1\\u6570\"]}, {\"cell_type\": \"code\", \"execution_count\": 23, \"metadata\": {}, \"outputs\": [], \"source\": [\"from collections import Counter\\n\", \"recommended_keywords_counter = Counter()\\n\", \"subscribed_keywords_counter = Counter()\"]}, {\"cell_type\": \"code\", \"execution_count\": 24, \"metadata\": {}, \"outputs\": [], \"source\": [\"for topic in recommended_topics:\\n\", \" recommended_keywords_counter.update(topic.keywords.split() if topic.keywords else [])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u201c\\u63a8\\u8350\\u5173\\u6ce8\\u201d\\u4e00\\u5171\\u67091164\\u4e2a\\u5173\\u952e\\u8bcd\"]}, {\"cell_type\": \"code\", \"execution_count\": 25, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"1164\"]}, \"execution_count\": 25, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"len(recommended_keywords_counter)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u201c\\u63a8\\u8350\\u5173\\u6ce8\\u201d\\u4e2d\\u51fa\\u73b0\\u9891\\u6b21\\u6700\\u9ad8\\u768410\\u4e2a\\u5173\\u952e\\u8bcd\\u53ca\\u5176\\u5bf9\\u5e94\\u9891\\u6570\"]}, {\"cell_type\": \"code\", \"execution_count\": 26, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"[('\\u641e\\u7b11', 14),\\n\", \" ('\\u7535\\u5f71', 12),\\n\", \" ('\\u660e\\u661f', 10),\\n\", \" ('\\u7efc\\u827a', 9),\\n\", \" ('\\u79d1\\u6280', 9),\\n\", \" ('\\u5a31\\u4e50', 8),\\n\", \" ('\\u9e7f\\u6657', 7),\\n\", \" ('\\u65b0\\u95fb', 6),\\n\", \" ('\\u674e\\u6613\\u5cf0', 6),\\n\", \" ('\\u65e5\\u672c', 6)]\"]}, \"execution_count\": 26, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"recommended_keywords_counter.most_common(10)\"]}, {\"cell_type\": \"code\", \"execution_count\": 27, \"metadata\": {}, \"outputs\": [], \"source\": [\"for topic in my_subscribed_topics:\\n\", \" subscribed_keywords_counter.update(topic.keywords.split() if topic.keywords else [])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u201c\\u6211\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\\u201d\\u4e00\\u5171\\u6709953\\u4e2a\\u5173\\u952e\\u8bcd\"]}, {\"cell_type\": \"code\", \"execution_count\": 28, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"953\"]}, \"execution_count\": 28, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"len(subscribed_keywords_counter)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u201c\\u6211\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\\u201d\\u4e2d\\u51fa\\u73b0\\u9891\\u6b21\\u6700\\u9ad8\\u768410\\u4e2a\\u5173\\u952e\\u8bcd\\u53ca\\u5176\\u5bf9\\u5e94\\u9891\\u6570\"]}, {\"cell_type\": \"code\", \"execution_count\": 29, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"[('\\u9605\\u8bfb', 13),\\n\", \" ('\\u641e\\u7b11', 12),\\n\", \" ('\\u79d1\\u666e', 11),\\n\", \" ('\\u70ed\\u95e8', 11),\\n\", \" ('\\u604b\\u7231', 10),\\n\", \" ('\\u597d\\u7b11', 10),\\n\", \" ('\\u65b0\\u95fb', 10),\\n\", \" ('\\u79d1\\u6280', 9),\\n\", \" ('\\u54c4\\u59b9\\u5b50', 9),\\n\", \" ('\\u54c4', 9)]\"]}, \"execution_count\": 29, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"subscribed_keywords_counter.most_common(10)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u597d\\u5947\\uff0c\\u600e\\u4e48\\u4f1a\\u6709 \\u201c\\u54c4\\u59b9\\u5b50\\u201d \\u5462\\uff1f \\ud83e\\udd14\\ud83e\\udd14\\ud83e\\udd14\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"-----\\n\", \"\\n\", \"\\u57fa\\u4e8e [Word Cloud](https://github.com/amueller/word_cloud) \\u751f\\u6210\\u5173\\u952e\\u8bcd\\u8bcd\\u4e91\"]}, {\"cell_type\": \"code\", \"execution_count\": 30, \"metadata\": {}, \"outputs\": [], \"source\": [\"from PIL import Image\\n\", \"import numpy as np\\n\", \"import matplotlib.pyplot as plt\\n\", \"from matplotlib.gridspec import GridSpec\\n\", \"from wordcloud import WordCloud\"]}, {\"cell_type\": \"code\", \"execution_count\": 31, \"metadata\": {}, \"outputs\": [{\"data\": {\"image/png\": \"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\\n\", \"text/plain\": [\"\"]}, \"execution_count\": 31, \"metadata\": {}, \"output_type\": \"display_data\"}], \"source\": [\"%matplotlib inline\\n\", \"mask = np.array(Image.open('jike.png'))\\n\", \"wc = WordCloud(background_color='white', max_words=100, mask=mask, width=1000, height=800,\\n\", \" relative_scaling=0.3, random_state=42,\\n\", \" font_path='/System/Library/Fonts/PingFang.ttc')\\n\", \"\\n\", \"the_grid = GridSpec(1, 2)\\n\", \"wc.generate_from_frequencies(recommended_keywords_counter)\\n\", \"plt.subplot(the_grid[0, 0], aspect=1)\\n\", \"plt.title('Recommendation World Cloud')\\n\", \"plt.axis(\\\"off\\\")\\n\", \"plt.imshow(wc, interpolation=\\\"bilinear\\\")\\n\", \"\\n\", \"wc.generate_from_frequencies(subscribed_keywords_counter)\\n\", \"plt.subplot(the_grid[0, 1], aspect=1)\\n\", \"plt.title('Subscription World Cloud')\\n\", \"plt.axis(\\\"off\\\")\\n\", \"plt.imshow(wc, interpolation=\\\"bilinear\\\")\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u6211\\u89c9\\u5f97\\u5427\\uff0c\\u201c\\u63a8\\u8350\\u5173\\u6ce8\\u201d\\u8bcd\\u4e91\\u91cc\\u90a3\\u4e48\\u5927\\u7684\\u201c\\u9e7f\\u6657\\u201d\\uff0c\\u201c\\u674e\\u6613\\u5cf0\\u201d\\uff0c\\u201c\\u7efc\\u827a\\u201d\\uff0c\\u201c\\u660e\\u661f\\u201d\\u548c\\u6211\\u7684\\u4e2a\\u4eba\\u6c14\\u8d28\\u4e0d\\u5927\\u7b26\\u5408\\u554a \\ud83e\\udd14\\n\", \"\\n\", \"\\u5373\\u523b\\u7684\\u63a8\\u8350\\u7cfb\\u7edf\\u8fd8\\u6709\\u5f88\\u5927\\u7684\\u8fdb\\u6b65\\u7a7a\\u95f4\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u7531\\u90e8\\u5206\\u6570\\u636e\\u5206\\u6790\\u5373\\u53cb\\u7684\\u5206\\u5e03\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u83b7\\u53d6 [\\u6211\\u5c31\\u60f3\\u5b9a\\u4e2a\\u4f4d](https://web.okjike.com/topic/5aaf50b9127e30001759c57e/user) \\u4e3b\\u9898\\u4e0b\\u6240\\u6709\\u7684\\u5e7f\\u573a\\u52a8\\u6001\"]}, {\"cell_type\": \"code\", \"execution_count\": 32, \"metadata\": {}, \"outputs\": [], \"source\": [\"square = c.get_topic_square(topic_id='5aaf50b9127e30001759c57e')\"]}, {\"cell_type\": \"code\", \"execution_count\": 33, \"metadata\": {}, \"outputs\": [], \"source\": [\"from collections import defaultdict\\n\", \"city_counter = defaultdict(int)\\n\", \"locations = []\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u8bb0\\u5f55\\u52a8\\u6001\\u7684\\u5b9a\\u4f4d\\u57ce\\u5e02\\u548c\\u5b9a\\u4f4d\\u7ecf\\u7eac\\u5ea6\"]}, {\"cell_type\": \"code\", \"execution_count\": 34, \"metadata\": {}, \"outputs\": [], \"source\": [\"while square.load_more_key:\\n\", \" more = square.load_more()\\n\", \" for post in more:\\n\", \" if post.type == 'ORIGINAL_POST' and post.poi:\\n\", \" city_counter[post.poi['cityname']] += 1\\n\", \" locations.append(post.poi['location'])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u6b64\\u4e3b\\u9898\\u5e7f\\u573a\\u4e0b\\u5317\\u4eac\\u5e02\\u7684\\u52a8\\u6001\\u6700\\u591a\"]}, {\"cell_type\": \"code\", \"execution_count\": 35, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"('\\u5317\\u4eac\\u5e02', 34)\"]}, \"execution_count\": 35, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"max(city_counter.items(), key=lambda i: i[1])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u4e00\\u5171\\u6709406\\u4e2a\\u7ecf\\u7eac\\u5ea6\\u4fe1\\u606f\"]}, {\"cell_type\": \"code\", \"execution_count\": 36, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"406\"]}, \"execution_count\": 36, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"len(locations)\"]}, {\"cell_type\": \"code\", \"execution_count\": 37, \"metadata\": {}, \"outputs\": [], \"source\": [\"from mpl_toolkits.basemap import Basemap\\n\", \"import matplotlib.pyplot as plt\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u58a8\\u7eff\\u8272\\u7684\\u6807\\u8bb0\\u70b9\\u5373\\u4e3a\\u52a8\\u6001\\u7684\\u53d1\\u5e03\\u5730\\u70b9\\uff0c\\u53ef\\u89c1\\uff1a\\n\", \"\\n\", \"- \\u201c\\u5317\\u4e0a\\u5e7f\\u201d\\u7eff\\u70b9\\u5bc6\\u96c6\\n\", \"- \\u5373\\u53cb\\u4e3b\\u8981\\u5728\\u4e2d\\u4e1c\\u90e8\\u5730\\u533a\\n\", \"- \\u53f0\\u6e7e\\u5730\\u533a\\uff0c\\u97e9\\u56fd \\ud83c\\uddf0\\ud83c\\uddf7\\u548c\\u65e5\\u672c \\ud83c\\uddef\\ud83c\\uddf5\\u90fd\\u6709\\u5373\\u53cb\\u54e6\"]}, {\"cell_type\": \"code\", \"execution_count\": 38, \"metadata\": {}, \"outputs\": [{\"data\": {\"image/png\": 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\\n\", \"text/plain\": [\"\"]}, \"metadata\": {}, \"output_type\": \"display_data\", \"execution_count\": 38}], \"source\": [\"%matplotlib inline\\n\", \"plt.figure(figsize=(20,10))\\n\", \"map = Basemap(projection='merc', llcrnrlon=70, llcrnrlat=15,\\n\", \" urcrnrlon=140, urcrnrlat=55, lat_0=15, lon_0=95, resolution='h')\\n\", \"map.drawcoastlines(linewidth=0.25)\\n\", \"map.drawcountries(linewidth=0.25)\\n\", \"map.fillcontinents(color='coral',lake_color='aqua')\\n\", \"map.drawmapboundary(fill_color='aqua')\\n\", \"for loc in locations:\\n\", \" x, y = map(*loc)\\n\", \" map.plot(x, y, 'go', markersize=4)\\n\", \"plt.title(' Jike POI Distribution ')\\n\", \"plt.show()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"-----------\\n\", \"\\n\", \"Prev: \\u4e58\\u8f66\\u6307\\u5357 \\ud83d\\ude87\\n\", \"Next: Jike Metro \\ud83d\\ude87 \\u4e2d\\u5404\\u4e2a\\u7c7b\\u7684\\u53ef\\u7528\\u5c5e\\u6027\"]}], \"metadata\": {\"anaconda-cloud\": {}, \"kernelspec\": {\"display_name\": \"Python [default]\", \"language\": \"python\", \"name\": \"python3\"}, \"language_info\": {\"codemirror_mode\": {\"name\": \"ipython\", \"version\": 3}, \"file_extension\": \".py\", \"mimetype\": \"text/x-python\", \"name\": \"python\", \"nbconvert_exporter\": \"python\", \"pygments_lexer\": \"ipython3\", \"version\": \"3.6.4\"}}, \"nbformat\": 4, \"nbformat_minor\": 2}" }, { "path": "docs/source_notebooks/index.ipynb", "content": "{\"cells\": [{\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"# \\u4e58\\u8f66\\u6307\\u5357\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"Prev: None\\n\", \"Next: \\u7b80\\u5355\\u7684\\u4f8b\\u5b50 \\ud83c\\udf30\"]}, {\"cell_type\": \"code\", \"execution_count\": 1, \"metadata\": {}, \"outputs\": [], \"source\": [\"import jike\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u521b\\u5efa\\u5373\\u523b\\u5ba2\\u6237\\u7aef\\n\", \"\\n\", \"\\u521d\\u6b21\\u4f7f\\u7528\\u4f1a\\u8981\\u6c42\\u4f7f\\u7528\\u5373\\u523bApp\\u626b\\u63cf\\u4e8c\\u7ef4\\u7801\\u767b\\u5f55\\n\", \"\\n\", \"\\u767b\\u5f55\\u6210\\u529f\\u4e4b\\u540e\\uff0c\\u4f1a\\u5728`~/.local/jike/jike_metro.json`\\u5b58\\u50a8\\u5373\\u523b\\u7684`auth_token`\\uff0c\\u540e\\u7eed\\u7684\\u4f7f\\u7528\\u5219\\u4f1a\\u8df3\\u8fc7\\u626b\\u63cf\\u4e8c\\u7ef4\\u7801\\u7684\\u6b65\\u9aa4\"]}, {\"cell_type\": \"code\", \"execution_count\": 2, \"metadata\": {}, \"outputs\": [], \"source\": [\"c = jike.JikeClient()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u67e5\\u770b\\u81ea\\u5df1\\u7684\\u7528\\u6237\\u4fe1\\u606f\\n\", \"\\n\", \"\\u8c03\\u7528\\uff1a `get_my_profile()` \\n\", \"\\u8fd4\\u56de\\uff1a `User`\\uff0c\\u81ea\\u5df1\\u7684\\u7528\\u6237\\u4fe1\\u606f \\uff08\\u57fa\\u4e8e`collection.namedtuple`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 3, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"User(screenName=\\u6316\\u5730\\u9053\\u7684)\"]}, \"execution_count\": 3, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_profile = c.get_my_profile()\\n\", \"my_profile\"]}, {\"cell_type\": \"code\", \"execution_count\": 4, \"metadata\": {}, \"outputs\": [{\"execution_count\": 4, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"\\u6316\\u5730\\u9053\\u7684\\n\", \"\\u24bf \\u9547-\\u5730\\u4e0b\\u5de5\\u4f5c\\u8005 \\ud83d\\udc77\\n\"]}], \"source\": [\"print(my_profile.screenName)\\n\", \"print(my_profile.briefIntro)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u83b7\\u53d6\\u81ea\\u5df1\\u7684\\u6536\\u85cf\\n\", \"\\n\", \"\\u8c03\\u7528\\uff1a `get_my_collection()` \\n\", \"\\u8fd4\\u56de\\uff1a `List`\\uff0c\\u81ea\\u5df1\\u7684\\u6536\\u85cf \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 5, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(20 items)\"]}, \"execution_count\": 5, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_collection = c.get_my_collection()\\n\", \"my_collection\"]}, {\"cell_type\": \"code\", \"execution_count\": 6, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OfficialMessage(id=55dd572f41904d0e00fc58f8, content=\\u5373\\u523b\\u679c\\u679c: \\u5206\\u4eab\\u4e00\\u53ea\\u66fe\\u7ecf\\u7684\\u7ae5\\u661f\\uff08\\u5df2\\u5149\\u901f\\u6210\\u957f\\uff09)\"]}, \"execution_count\": 6, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_collection[0]\"]}, {\"cell_type\": \"code\", \"execution_count\": 7, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"39\"]}, \"execution_count\": 7, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"guoguo = my_collection[0]\\n\", \"guoguo.likeCount\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u52a0\\u8f7d\\u6240\\u6709\\u6536\\u85cf\"]}, {\"cell_type\": \"code\", \"execution_count\": 8, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(24 items)\"]}, \"execution_count\": 8, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_collection.load_all()\\n\", \"my_collection\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u6d41\\u5f0f\\u83b7\\u53d6\\u9996\\u9875\\u6d88\\u606f\\u548c\\u52a8\\u6001\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u6d88\\u606f \\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_news_feed()` \\n\", \" \\u8fd4\\u56de\\uff1a `Stream`\\uff0c\\u6d88\\u606f\\u6d41 \\uff08\\u57fa\\u4e8e`collection.deque`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 9, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Stream(20 items, with 200 capacity)\"]}, \"execution_count\": 9, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"news_feed = c.get_news_feed()\\n\", \"news_feed\"]}, {\"cell_type\": \"code\", \"execution_count\": 10, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OfficialMessage(id=5ac392c18fecf20017ec27e7, content=\\u59d1\\u59d1\\u4f4f\\u8fdb\\u4e86\\u517b\\u8001\\u9662)\"]}, \"execution_count\": 10, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"news_feed[0]\"]}, {\"cell_type\": \"code\", \"execution_count\": 11, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Stream(40 items, with 200 capacity)\"]}, \"execution_count\": 11, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"news_feed.load_more()\\n\", \"news_feed\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u52a8\\u6001 \\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_following_update()` \\n\", \" \\u8fd4\\u56de\\uff1a `Stream`\\uff0c\\u52a8\\u6001\\u6d41 \\uff08\\u57fa\\u4e8e`collection.deque`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 12, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Stream(29 items, with 200 capacity)\"]}, \"execution_count\": 12, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"following_update = c.get_following_update()\\n\", \"following_update\"]}, {\"cell_type\": \"code\", \"execution_count\": 13, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OriginalPost(id=5ac392cd3535890017f8a3bb, content=\\u6240\\u4ee5\\u300c\\u5411\\u62c9\\u65af\\u7ef4\\u52a0\\u65af\\u5b66\\u4e60\\u300d\\uff081972\\uff09\\u4e5f\\u53ef\\u4ee5\\u662f\\u5411\\u4e1c\\u4eac\\u5b66\\u4e60\\u3002\\u800c\\u4e14\\u4e1c\\u4eac\\u66f4\\u65e9\\u3002#ATokyoMemoir #IanBuruma)\"]}, \"execution_count\": 13, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"following_update[0]\"]}, {\"cell_type\": \"code\", \"execution_count\": 14, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Stream(45 items, with 200 capacity)\"]}, \"execution_count\": 14, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"following_update.load_more()\\n\", \"following_update\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u5f53\\u524d\\u672a\\u8bfb\\u6d88\\u606f\\u6570 \\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a`get_news_feed_unread_count()` \\n\", \" \\u8fd4\\u56de\\uff1a`int`\\uff0c\\u672a\\u8bfb\\u6d88\\u606f\\u6570\"]}, {\"cell_type\": \"code\", \"execution_count\": 15, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"0\"]}, \"execution_count\": 15, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.get_news_feed_unread_count()\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u7684\\u7528\\u6237\\u4fe1\\u606f\\u3001\\u53d1\\u5e03\\u7684\\u52a8\\u6001\\u3001\\u521b\\u5efa\\u7684\\u4e3b\\u9898\\u3001\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\\u3001TA\\u5173\\u6ce8\\u7684\\u4eba\\u548c\\u5173\\u6ce8TA\\u7684\\u4eba\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u7684\\u7528\\u6237\\u4fe1\\u606f \\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_profile(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d\\uff08\\u6ce8\\uff1a\\u4e0d\\u662f\\u7528\\u6237\\u7684\\u663e\\u793a\\u540d\\uff0c\\u800c\\u662f\\u7c7b\\u4f3c [\\u74e6\\u603b\\u4e2a\\u4eba\\u9875\\u9762](https://web.okjike.com/user/82D23B32-CF36-4C59-AD6F-D05E3552CBF3/) \\u5728\\u6d4f\\u89c8\\u5668\\u5730\\u5740\\u680f\\u4e2d `https://web.okjike.com/user/` \\u540e\\u7684\\u90e8\\u5206\\uff09 \\n\", \" \\u8fd4\\u56de\\uff1a `User`, \\u7528\\u6237\\u4fe1\\u606f \\uff08\\u57fa\\u4e8e`collection.namedtuple`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 16, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"User(screenName=\\u74e6\\u6041)\"]}, \"execution_count\": 16, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo = c.get_user_profile(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo\"]}, {\"cell_type\": \"code\", \"execution_count\": 17, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"'\\u5373\\u523bCEO'\"]}, \"execution_count\": 17, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo.briefIntro\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u53d1\\u5e03\\u7684\\u52a8\\u6001 \\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_post(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d \\n\", \" \\u8fd4\\u56de\\uff1a `List`, \\u7528\\u6237\\u53d1\\u5e03\\u7684\\u52a8\\u6001 \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 18, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(20 items)\"]}, \"execution_count\": 18, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts = c.get_user_post(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_posts\"]}, {\"cell_type\": \"code\", \"execution_count\": 19, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Repost(id=5ac36c4913fd9f0018a1a5ca, content=\\u201c\\u6218\\u7565\\u662f\\u73b0\\u5b9e\\u548c\\u7406\\u60f3\\u7684\\u7ed3\\u5408\\u3002\\u4f1f\\u5927\\u7684\\u6218\\u7565\\u662f\\u6781\\u7aef\\u7684\\u73b0\\u5b9e\\u4e3b\\u4e49\\u548c\\u6781\\u7aef\\u7684\\u7406\\u60f3\\u4e3b\\u4e49\\u7ed3\\u5408\\u7684\\u4ea7\\u7269\\u201d)\"]}, \"execution_count\": 19, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts[0]\"]}, {\"cell_type\": \"code\", \"execution_count\": 20, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(59 items)\"]}, \"execution_count\": 20, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts.load_more()\\n\", \"ceo_posts\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`load_all` \\u7ea6\\u8fd0\\u884c\\u4e8610s\"]}, {\"cell_type\": \"code\", \"execution_count\": 21, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(3011 items)\"]}, \"execution_count\": 21, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts.load_all()\\n\", \"ceo_posts\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u74e6\\u603b\\u7684\\u7b2c\\u4e00\\u6761\\u52a8\\u6001\"]}, {\"cell_type\": \"code\", \"execution_count\": 22, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Repost(id=5ab20efc63cd65165515d4e2, content=\\u60f3\\u7ed9\\u8fd9\\u4e2a\\u4e3b\\u9898\\u6253\\u94b1)\"]}, \"execution_count\": 22, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts[-1]\"]}, {\"cell_type\": \"code\", \"execution_count\": 23, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"'2016-09-14T11:30:58.283Z'\"]}, \"execution_count\": 23, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_posts[-1].createdAt\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u521b\\u5efa\\u7684\\u4e3b\\u9898\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_created_topic(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d \\n\", \" \\u8fd4\\u56de\\uff1a `List`, \\u7528\\u6237\\u521b\\u5efa\\u7684\\u4e3b\\u9898 \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 24, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Topic(id=5a8eeb3d4eb3b0001858fa87, content=\\u53c8\\u6709\\u4eba\\u5728\\u5fae\\u535a\\u63d0\\u5230yes prime minister\\u4e86)\"]}, \"execution_count\": 24, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_created_topics = c.get_user_created_topic(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_created_topics[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u5173\\u6ce8\\u7684\\u4e3b\\u9898\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_subscribed_topic(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d \\n\", \" \\u8fd4\\u56de\\uff1a `List`, \\u7528\\u6237\\u5173\\u6ce8\\u7684\\u4e3b\\u9898 \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 25, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Topic(id=5a41c69600074100168fd2a1, content=\\u53c8\\u6709\\u4eba\\u5728\\u5fae\\u535a\\u63d0\\u5230Reddit)\"]}, \"execution_count\": 25, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_subscribed_topics = c.get_user_subscribed_topic(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_subscribed_topics[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u7528\\u6237\\u5173\\u6ce8\\u7684\\u4eba\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_following(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d \\n\", \" \\u8fd4\\u56de\\uff1a `List`, \\u7528\\u6237\\u5173\\u6ce8\\u7684\\u4eba \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 26, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"User(screenName=\\u8c0c\\u8c0c)\"]}, \"execution_count\": 26, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_followings = c.get_user_following(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_followings[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u5173\\u6ce8\\u67d0\\u4e2a\\u7528\\u6237\\u7684\\u4eba\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_user_follower(username)` \\n\", \" \\u53c2\\u6570\\uff1a `username`: \\u6307\\u5b9a\\u7528\\u6237\\u7684\\u7528\\u6237\\u540d \\n\", \" \\u8fd4\\u56de\\uff1a `List`, \\u5173\\u6ce8\\u6307\\u5b9a\\u7528\\u6237\\u7684\\u4eba \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 27, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"User(screenName=\\u590f\\u6d1b\\u514b\\u724c\\u82b1\\u751f\\u9171)\"]}, \"execution_count\": 27, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"ceo_followers = c.get_user_follower(username='82D23B32-CF36-4C59-AD6F-D05E3552CBF3')\\n\", \"ceo_followers[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u83b7\\u53d6\\u67d0\\u4e2a\\u4e3b\\u9898\\u4e0b\\u7684\\u7cbe\\u9009\\u548c\\u5e7f\\u573a\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u4e3b\\u9898\\u4e0b\\u7684\\u7cbe\\u9009\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_topic_selected(topic_id)` \\n\", \" \\u53c2\\u6570\\uff1a `topic_id`: \\u6307\\u5b9a\\u4e3b\\u9898\\u7684id\\uff0c\\u7c7b\\u4f3c\\u4e8e [\\u4e0d\\u597d\\u7b11\\u4fbf\\u5229\\u5e97](https://web.okjike.com/topic/5701d10d5002b912000e588d/official) \\u5730\\u5740\\u680f\\u90e8\\u5206\\u5728 `https://web.okjike.com/topic/`\\u4e4b\\u540e\\u7684\\u90e8\\u5206 \\n\", \" \\u8fd4\\u56de\\uff1a `Stream`, \\u4e3b\\u9898\\u7cbe\\u9009\\uff08\\u57fa\\u4e8e`collection.deque`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 28, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OfficialMessage(id=5ac371e0a7476600171f1a31, content=\\u5e72\\u6b7b\\u7f8e\\u56e2 \\uff0c\\u78be\\u538b\\u6ef4\\u6ef4\\uff01\\u997f\\u4e86\\u4e48\\u548c\\u4f60\\u4e00\\u8d77\\u62fc)\"]}, \"execution_count\": 28, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"topic_selected = c.get_topic_selected(topic_id='5701d10d5002b912000e588d')\\n\", \"topic_selected[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u83b7\\u53d6\\u67d0\\u4e2a\\u4e3b\\u9898\\u4e0b\\u7684\\u5e7f\\u573a\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `get_topic_square(topic_id)` \\n\", \" \\u53c2\\u6570\\uff1a `topic_id`: \\u6307\\u5b9a\\u4e3b\\u9898\\u7684id \\n\", \" \\u8fd4\\u56de\\uff1a `Stream`, \\u4e3b\\u9898\\u5e7f\\u573a\\uff08\\u57fa\\u4e8e`collection.deque`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 29, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OriginalPost(id=5a43395fc912390015ec6b6a, content=\\u4e0d\\u6b63\\u7ecf\\uff0c\\u4e0d\\u4e0a\\u7eb2\\u4e0a\\u7ebf\\uff0c\\u5076\\u5c14\\u5f00\\u8f66\\uff0c\\u5f53\\u7136\\u4e5f\\u6b22\\u8fce\\u4f60\\u6765\\u4ee3\\u9a7e\\u3002)\"]}, \"execution_count\": 29, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"topic_square = c.get_topic_square(topic_id='5701d10d5002b912000e588d')\\n\", \"topic_square[0]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u83b7\\u53d6\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\u7684\\u8bc4\\u8bba\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u8c03\\u7528\\uff1a `get_comment(message)` \\n\", \"\\u53c2\\u6570\\uff1a `message`: \\u8981\\u83b7\\u53d6\\u8bc4\\u8bba\\u7684\\u6d88\\u606f/\\u52a8\\u6001 \\n\", \"\\u8fd4\\u56de\\uff1a `Stream`, \\u8bc4\\u8bba \\uff08\\u57fa\\u4e8e`collection.deque`\\uff09 \"]}, {\"cell_type\": \"code\", \"execution_count\": 30, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Stream(13 items, with 200 capacity)\"]}, \"execution_count\": 30, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"comments = c.get_comment(topic_square[0])\\n\", \"comments\"]}, {\"cell_type\": \"code\", \"execution_count\": 31, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Comment(id=5a4341eb57b9c60010c707cb, content=\\u795e\\u7279\\u4e48\\u5076\\u5c14\\u3002\\u4e0d\\u662f\\u5728\\u5f00\\u8f66\\u5c31\\u662f\\u5728\\u627e\\u8f66\\u5427)\"]}, \"execution_count\": 31, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"max(comments, key=lambda c: c.likeCount)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u5728\\u6d4f\\u89c8\\u5668\\u4e2d\\u6253\\u5f00\\u67d0\\u6761\\u6d88\\u606f\\u7684\\u539f\\u59cb\\u94fe\\u63a5\\n\", \"\\n\", \"\\u8c03\\u7528\\uff1a`open_in_browser(url_or_message)` \\n\", \"\\u53c2\\u6570\\uff1a`url_or_message`: url \\u6216\\u8005 message (\\u4f8b\\u5982`topic_selected[0]`\\uff0c\\u8fd9\\u662f\\u4e00\\u6761`OfficialMessage`) \\n\", \"\\u8fd4\\u56de\\uff1a`None`\"]}, {\"cell_type\": \"code\", \"execution_count\": 32, \"metadata\": {}, \"outputs\": [], \"source\": [\"c.open_in_browser(topic_selected[0])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u53d1\\u5e03\\u4e2a\\u4eba\\u52a8\\u6001\\uff08\\u53ef\\u5e26\\u56fe\\u3001\\u5e26\\u94fe\\u63a5\\u3001\\u5e26\\u4e3b\\u9898\\uff09\\n\", \"\\n\", \"\\u8c03\\u7528\\uff1a `create_my_post(content, link, topic_id, pictures)` \\n\", \"\\u53c2\\u6570\\uff1a `content`: \\u8981\\u53d1\\u5e03\\u7684\\u5185\\u5bb9 / `link`: \\u6240\\u5e26\\u7684\\u94fe\\u63a5 / `topic_id`: \\u6240\\u5e26\\u4e3b\\u9898\\u7684id / `pictures`: \\u6240\\u5e26\\u56fe\\u7684\\u672c\\u5730\\u5730\\u5740\\uff08\\u6ce8\\uff1a\\u7531\\u4e8e\\u5373\\u523b\\u7684\\u9650\\u5236\\uff0c\\u52a8\\u6001\\u4e0d\\u80fd\\u540c\\u65f6\\u5e26\\u6709\\u56fe\\u7247\\u548c\\u94fe\\u63a5\\uff09 \\n\", \"\\u8fd4\\u56de\\uff1a `OriginalPost`\\uff0c\\u6240\\u53d1\\u5e03\\u7684\\u52a8\\u6001 \\uff08\\u57fa\\u4e8e`collection.namedtuple`\\uff09\"]}, {\"cell_type\": \"code\", \"execution_count\": 33, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"OriginalPost(id=5ac3990c2391fb00174e3843, content=Hello world from Jike Metro \\ud83d\\ude87!)\"]}, \"execution_count\": 33, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"my_new_post = c.create_my_post('Hello world from Jike Metro \\ud83d\\ude87!')\\n\", \"my_new_post\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u70b9\\u8d5e\\u3001\\u6536\\u85cf\\u3001\\u8bc4\\u8bba\\u3001\\u8f6c\\u53d1\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001 \"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u70b9\\u8d5e\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `like_it(message)` \\n\", \" \\u53c2\\u6570\\uff1a `message`: \\u8981\\u8d5e\\u7684\\u6d88\\u606f/\\u52a8\\u6001 \\n\", \" \\u8fd4\\u56de\\uff1a `Bool`, \\u6210\\u529f\\u4e0e\\u5426\"]}, {\"cell_type\": \"code\", \"execution_count\": 34, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"True\"]}, \"execution_count\": 34, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.like_it(my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u53d6\\u6d88\\u8d5e\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `unlike_it(message)` \\n\", \" \\u53c2\\u6570\\uff1a `message`: \\u8981\\u53d6\\u6d88\\u8d5e\\u7684\\u6d88\\u606f/\\u52a8\\u6001 \\n\", \" \\u8fd4\\u56de\\uff1a `Bool`, \\u6210\\u529f\\u4e0e\\u5426\"]}, {\"cell_type\": \"code\", \"execution_count\": 35, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"True\"]}, \"execution_count\": 35, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.unlike_it(my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u6536\\u85cf\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `collect_it(message)` \\n\", \" \\u53c2\\u6570\\uff1a `message`: \\u8981\\u6536\\u85cf\\u7684\\u6d88\\u606f/\\u52a8\\u6001 \\n\", \" \\u8fd4\\u56de\\uff1a `Bool`, \\u6210\\u529f\\u4e0e\\u5426\"]}, {\"cell_type\": \"code\", \"execution_count\": 36, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"True\"]}, \"execution_count\": 36, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.collect_it(my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u53d6\\u6d88\\u6536\\u85cf\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `uncollect_it(message)` \\n\", \" \\u53c2\\u6570\\uff1a `message`: \\u8981\\u53d6\\u6d88\\u6536\\u85cf\\u7684\\u6d88\\u606f/\\u52a8\\u6001 \\n\", \" \\u8fd4\\u56de\\uff1a `Bool`, \\u6210\\u529f\\u4e0e\\u5426\"]}, {\"cell_type\": \"code\", \"execution_count\": 37, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"True\"]}, \"execution_count\": 37, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.uncollect_it(my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u8f6c\\u53d1\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `repost_it(content, message, sync_comment)` \\n\", \" \\u53c2\\u6570\\uff1a `content`: \\u8f6c\\u53d1\\u7684\\u8bc4\\u8bba\\u5185\\u5bb9 / `message`: \\u8981\\u8f6c\\u53d1\\u7684\\u6d88\\u606f\\u6216\\u52a8\\u6001 / `sync_comment`: \\u662f\\u5426\\u540c\\u6b65\\u8bc4\\u8bba\\uff0c\\u9ed8\\u8ba4\\u4e3a`True` \\n\", \" \\u8fd4\\u56de\\uff1a `Repost`, \\u53d1\\u5e03\\u7684\\u8f6c\\u53d1 \\uff08\\u57fa\\u4e8e`collection.namedtuple`)\"]}, {\"cell_type\": \"code\", \"execution_count\": 38, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Repost(id=5ac399fd44f1020018533abc, content=Support Jike Metro \\ud83d\\ude87)\"]}, \"execution_count\": 38, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.repost_it('Support Jike Metro \\ud83d\\ude87', my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"- \\u8bc4\\u8bba\\u67d0\\u6761\\u6d88\\u606f/\\u52a8\\u6001\\n\", \"\\n\", \" \\u8c03\\u7528\\uff1a `comment_it(content, message, pictures, sync2personal_updates)` \\n\", \" \\u53c2\\u6570\\uff1a `content`: \\u8981\\u8bc4\\u8bba\\u5185\\u5bb9 / `message`: \\u8981\\u8bc4\\u8bba\\u7684\\u6d88\\u606f\\u6216\\u52a8\\u6001 / `pictures`: \\u6240\\u5e26\\u56fe\\u7684\\u672c\\u5730\\u5730\\u5740 / `sync2personal_updates`: \\u662f\\u5426\\u540c\\u6b65\\u5230\\u4e2a\\u4eba\\u52a8\\u6001\\uff0c\\u9ed8\\u8ba4\\u4e3a`True` \\n\", \" \\u8fd4\\u56de\\uff1a `Comment`, \\u53d1\\u5e03\\u7684\\u8bc4\\u8bba \\uff08\\u57fa\\u4e8e`collection.namedtuple`)\"]}, {\"cell_type\": \"code\", \"execution_count\": 39, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"Comment(id=5ac39a364ce4cf001702d51a, content=Upvote for Jike Metro \\ud83d\\ude87)\"]}, \"execution_count\": 39, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.comment_it('Upvote for Jike Metro \\ud83d\\ude87', my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u5220\\u9664\\u4e2a\\u4eba\\u52a8\\u6001\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u8c03\\u7528\\uff1a `delete_my_post(post)` \\n\", \"\\u53c2\\u6570\\uff1a `post`: \\u8981\\u5220\\u9664\\u7684\\u52a8\\u6001 \\n\", \"\\u8fd4\\u56de\\uff1a `Bool`, \\u6210\\u529f\\u4e0e\\u5426 \"]}, {\"cell_type\": \"code\", \"execution_count\": 40, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"True\"]}, \"execution_count\": 40, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"c.delete_my_post(my_new_post)\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"## \\u6839\\u636e\\u5173\\u952e\\u8bcd\\u641c\\u7d22\\u4e3b\\u9898\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u8c03\\u7528\\uff1a `search_topic(keywords)` \\n\", \"\\u53c2\\u6570\\uff1a `keywords`\\uff1a\\u641c\\u7d22\\u7684\\u5173\\u952e\\u8bcd \\n\", \"\\u8fd4\\u56de\\uff1a `List`\\uff0c \\u641c\\u7d22\\u5230\\u7684\\u4e3b\\u9898\\uff0c \\uff08\\u57fa\\u4e8e`collection.abc.Sequence`) \"]}, {\"cell_type\": \"code\", \"execution_count\": 41, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"List(20 items)\"]}, \"execution_count\": 41, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"topics = c.search_topic(keywords='apple')\\n\", \"topics\"]}, {\"cell_type\": \"code\", \"execution_count\": 42, \"metadata\": {}, \"outputs\": [{\"data\": {\"text/plain\": [\"['Apple Watch',\\n\", \" 'Apple Pay\\u65b0\\u52a8\\u5411',\\n\", \" '\\u4e0b\\u4e00\\u4ee3iPhone\\u6700\\u65b0\\u60c5\\u62a5',\\n\", \" '\\u5185\\u5730\\u6709\\u65b0\\u7684Apple Store\\u5f00\\u4e1a',\\n\", \" '\\u82f9\\u679c\\u6709\\u65b0\\u7684\\u5b98\\u65b9\\u89c6\\u9891',\\n\", \" '\\u82f9\\u679c\\u7279\\u60e0\\u65e5\\u63d0\\u9192',\\n\", \" '\\u6bcf\\u5929\\u4e00\\u4e2aMac\\u5feb\\u6377\\u952e\\u4ecb\\u7ecd',\\n\", \" 'Mac\\u786c\\u4ef6\\u65b0\\u54c1\\u53ca\\u7cfb\\u7edf\\u66f4\\u65b0\\u8ffd\\u8e2a',\\n\", \" 'Mac Stories\\u535a\\u5ba2\\u201cStories\\u201d\\u677f\\u5757\\u65b0\\u6587\\u7ae0',\\n\", \" 'Apple\\u7167\\u7247\\u66f4\\u65b0\\u63d0\\u9192',\\n\", \" 'Apple Newsroom \\u66f4\\u65b0',\\n\", \" 'apple\\u4ea7\\u54c1\\u4f18\\u60e0',\\n\", \" 'Apple\\u8d2d\\u4e70\\u63d0\\u9192',\\n\", \" 'Apple\\u7684\\u4e1c\\u897f\\u6709\\u66f4\\u65b0',\\n\", \" 'Apple \\u7167\\u7247 \\u66f4\\u65b0\\u63d0\\u9192',\\n\", \" 'Apple Music\\u7167\\u7247\\u66f4\\u65b0\\u63d0\\u9192',\\n\", \" 'Apple Store \\u6709\\u65b0\\u7684\\u5b98\\u7ffb\\u4ea7\\u54c1\\u4e0a\\u67b6',\\n\", \" '\\u6709\\u503c\\u5f97\\u4e70\\u7684APPLE\\u82f9\\u679c\\u4f18\\u60e0',\\n\", \" '\\u6709\\u503c\\u5f97\\u4e70\\u7684Apple watch\\u4f18\\u60e0',\\n\", \" 'Apple \\u5e7f\\u544a\\u6709\\u65b0\\u7684\\u914d\\u4e50']\"]}, \"execution_count\": 42, \"metadata\": {}, \"output_type\": \"execute_result\"}], \"source\": [\"[topic['content'] for topic in topics]\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"------\\n\", \"\\n\", \"Prev: None\\n\", \"Next: \\u7b80\\u5355\\u7684\\u4f8b\\u5b50 \\ud83c\\udf30\"]}], \"metadata\": {\"anaconda-cloud\": {}, \"kernelspec\": {\"display_name\": \"Python 3\", \"language\": \"python\", \"name\": \"python3\"}, \"language_info\": {\"codemirror_mode\": {\"name\": \"ipython\", \"version\": 3}, \"file_extension\": \".py\", \"mimetype\": \"text/x-python\", \"name\": \"python\", \"nbconvert_exporter\": \"python\", \"pygments_lexer\": \"ipython3\", \"version\": \"3.6.1\"}}, \"nbformat\": 4, \"nbformat_minor\": 2}\n" }, { "path": "docs/source_notebooks/jike_api.ipynb", "content": "{\"cells\": [{\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"# \\u5373\\u523bWeb API\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"Prev: Jike Metro \\ud83d\\ude87 \\u4e2d\\u5404\\u4e2a\\u7c7b\\u7684\\u53ef\\u7528\\u5c5e\\u6027\\n\", \"Next: None\"]}, {\"cell_type\": \"code\", \"execution_count\": 1, \"metadata\": {}, \"outputs\": [], \"source\": [\"from jike.constants import ENDPOINTS\"]}, {\"cell_type\": \"code\", \"execution_count\": 2, \"metadata\": {}, \"outputs\": [{\"execution_count\": 2, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"Function: create_session, URL: https://app.jike.ruguoapp.com/sessions.create\\n\", \"\\n\", \"Function: wait_login, URL: https://app.jike.ruguoapp.com/sessions.wait_for_login\\n\", \"\\n\", \"Function: confirm_login, URL: https://app.jike.ruguoapp.com/sessions.wait_for_confirmation\\n\", \"\\n\", \"Function: my_collections, URL: https://app.jike.ruguoapp.com/1.0/users/collections/list\\n\", \"\\n\", \"Function: news_feed, URL: https://app.jike.ruguoapp.com/1.0/newsFeed/list\\n\", \"\\n\", \"Function: news_feed_unread_count, URL: https://app.jike.ruguoapp.com//1.0/newsFeed/countUnreads\\n\", \"\\n\", \"Function: following_update, URL: https://app.jike.ruguoapp.com/1.0/personalUpdate/followingUpdates\\n\", \"\\n\", \"Function: user_profile, URL: https://app.jike.ruguoapp.com/1.0/users/profile\\n\", \"\\n\", \"Function: user_post, URL: https://app.jike.ruguoapp.com/1.0/personalUpdate/single\\n\", \"\\n\", \"Function: user_created_topic, URL: https://app.jike.ruguoapp.com/1.0/customTopics/custom/listCreated\\n\", \"\\n\", \"Function: user_subscribed_topic, URL: https://app.jike.ruguoapp.com/1.0/users/topics/listSubscribed\\n\", \"\\n\", \"Function: user_following, URL: https://app.jike.ruguoapp.com/1.0/userRelation/getFollowingList\\n\", \"\\n\", \"Function: user_follower, URL: https://app.jike.ruguoapp.com/1.0/userRelation/getFollowerList\\n\", \"\\n\", \"Function: topic_selected, URL: https://app.jike.ruguoapp.com/1.0/messages/history\\n\", \"\\n\", \"Function: topic_square, URL: https://app.jike.ruguoapp.com/1.0/squarePosts/list\\n\", \"\\n\", \"Function: list_comment, URL: https://app.jike.ruguoapp.com/1.0/comments/listPrimary\\n\", \"\\n\", \"Function: create_post, URL: https://app.jike.ruguoapp.com/1.0/originalPosts/create\\n\", \"\\n\", \"Function: delete_post, URL: https://app.jike.ruguoapp.com/1.0/originalPosts/remove\\n\", \"\\n\", \"Function: extract_link, URL: https://app.jike.ruguoapp.com/1.0/readability/extract\\n\", \"\\n\", \"Function: picture_uptoken, URL: https://upload.jike.ruguoapp.com/token\\n\", \"\\n\", \"Function: picture_upload, URL: https://up.qbox.me/\\n\", \"\\n\", \"Function: repost_it, URL: https://app.jike.ruguoapp.com/1.0/reposts/add\\n\", \"\\n\", \"Function: comment_it, URL: https://app.jike.ruguoapp.com/1.0/comments/add\\n\", \"\\n\", \"Function: search_topic, URL: https://app.jike.ruguoapp.com/1.0/users/topics/search\\n\", \"\\n\"]}], \"source\": [\"for k, v in ENDPOINTS.items():\\n\", \" if '{t}' not in v:\\n\", \" print('Function: {fn}, URL: {url}\\\\n'.format(fn=k, url=v))\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u4ee5\\u4e0b\\u7684API\\u8c03\\u7528\\u9700\\u8981\\u5bf9\\u5e94\\u7279\\u5b9a\\u7684\\u6d88\\u606f\\u7c7b\\u578b\\n\", \"\\n\", \"\\u5047\\u8bbe\\u6d88\\u606f\\u7c7b\\u578b\\u4e3a'ORIGINAL_MESSAGE', \\u5219\\u5bf9\\u5e94\\u7684URL\\u90e8\\u5206\\u4e3a'originalMessages'\"]}, {\"cell_type\": \"code\", \"execution_count\": 3, \"metadata\": {}, \"outputs\": [], \"source\": [\"message_type = 'originalMessages'\"]}, {\"cell_type\": \"code\", \"execution_count\": 4, \"metadata\": {}, \"outputs\": [{\"execution_count\": 4, \"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"Function: like_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/like\\n\", \"\\n\", \"Function: unlike_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/unlike\\n\", \"\\n\", \"Function: collect_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/collect\\n\", \"\\n\", \"Function: uncollect_it, URL: https://app.jike.ruguoapp.com/1.0/originalMessages/uncollect\\n\", \"\\n\"]}], \"source\": [\"for k, v in ENDPOINTS.items():\\n\", \" if '{t}' in v:\\n\", \" v = v.format(t=message_type)\\n\", \" print('Function: {fn}, URL: {url}\\\\n'.format(fn=k, url=v))\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"\\u5404\\u4e2aURL\\u5177\\u4f53\\u7684HTTP BODY\\u5728\\u8fd9\\u91cc\\u5c31\\u4e0d\\u8d58\\u8ff0\\u4e86\\uff0c\\u611f\\u5174\\u8da3\\u7684\\u53ef\\u4ee5\\u5728\\u6d4f\\u89c8\\u5668\\u8c03\\u8bd5\\u5668\\u91cc\\u8ffd\\u8e2a\\u67e5\\u770bAPI\\u8c03\\u7528\\u7684 json payload \\u548c json response\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"---------\\n\", \"\\n\", \"Prev: Jike Metro \\ud83d\\ude87 \\u4e2d\\u5404\\u4e2a\\u7c7b\\u7684\\u53ef\\u7528\\u5c5e\\u6027\\n\", \"Next: None\"]}], \"metadata\": {\"anaconda-cloud\": {}, \"kernelspec\": {\"display_name\": \"Python [default]\", \"language\": \"python\", \"name\": \"python3\"}, \"language_info\": {\"codemirror_mode\": {\"name\": \"ipython\", \"version\": 3}, \"file_extension\": \".py\", \"mimetype\": \"text/x-python\", \"name\": \"python\", \"nbconvert_exporter\": \"python\", \"pygments_lexer\": \"ipython3\", \"version\": \"3.6.4\"}}, \"nbformat\": 4, \"nbformat_minor\": 2}" }, { "path": "docs/source_notebooks/objects.ipynb", "content": "{\"cells\": [{\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"# Jike Metro \\ud83d\\ude87 \\u4e2d\\u5404\\u4e2a\\u7c7b\\u7684\\u53ef\\u7528\\u5c5e\\u6027\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"Prev: \\u7b80\\u5355\\u7684\\u4f8b\\u5b50 \\ud83c\\udf30\\n\", \"Next: \\u5373\\u523bWeb API\"]}, {\"cell_type\": \"code\", \"execution_count\": 1, \"metadata\": {}, \"outputs\": [], \"source\": [\"from jike.objects import *\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`User` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 2, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('areaCode', 'avatarImage', 'backgroundImage', 'bio', 'birthday')\\n\", \"('briefIntro', 'city', 'country', 'following', 'gender')\\n\", \"('id', 'initUsername', 'isBetaUser', 'isLoginUser', 'isVerified')\\n\", \"('mobilePhoneNumber', 'preferences', 'province', 'profileImageUrl', 'ref')\\n\", \"('qqOpenId', 'screenName', 'updatedAt', 'userId', 'username')\\n\", \"('usernameSet', 'verifyMessage', 'wechatOpenId', 'weiboUid', 'weiboUserInfo')\\n\", \"('followedCount', 'followingCount', 'highlightedPersonalUpdates', 'liked', 'topicCreated')\\n\", \"('topicSubscribed',)\\n\"], \"execution_count\": 2}], \"source\": [\"for i in range(len(User._fields) // 5 + 1):\\n\", \" print(User._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`Topic` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 3, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('briefIntro', 'content', 'createdAt', 'enableForUserPost', 'enablePictureComments')\\n\", \"('friendsAlsoSubscribe', 'id', 'isAnonymous', 'isDreamTopic', 'isValid')\\n\", \"('keywords', 'lastMessagePostTime', 'likeIcon', 'maintainer', 'messagePrefix')\\n\", \"('newCategory', 'operateStatus', 'pictureUrl', 'rectanglePicture', 'ref')\\n\", \"('squarePicture', 'subscribedAt', 'subscribedStatusRawValue', 'subscribersCount', 'thumbnailUrl')\\n\", \"('timeForRank', 'topicId', 'topicType', 'updatedAt')\\n\"], \"execution_count\": 3}], \"source\": [\"for i in range(len(Topic._fields) // 5 + 1):\\n\", \" print(Topic._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`OfficialMessage` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 4, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\\n\", \"('pictures', 'liked', 'likeIcon', 'collected', 'id')\\n\", \"('targetType', 'status', 'viewType', 'createdAt', 'target')\\n\", \"('likeCount', 'video', 'collectTime', 'abstract', 'content')\\n\", \"()\\n\"], \"execution_count\": 4}], \"source\": [\"for i in range(len(OfficialMessage._fields) // 5 + 1):\\n\", \" print(OfficialMessage._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`OriginalPost` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 5, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'poi', 'commentCount', 'read', 'messageId')\\n\", \"('isCommentForbidden', 'type', 'pictures', 'liked', 'likeIcon')\\n\", \"('collected', 'id', 'targetType', 'status', 'viewType')\\n\", \"('createdAt', 'target', 'likeCount', 'collectTime', 'content')\\n\", \"()\\n\"], \"execution_count\": 5}], \"source\": [\"for i in range(len(OriginalPost._fields) // 5 + 1):\\n\", \" print(OriginalPost._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`Repost` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 6, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\\n\", \"('pictures', 'liked', 'likeIcon', 'collected', 'id')\\n\", \"('syncCommentId', 'targetType', 'status', 'replyToComment', 'viewType')\\n\", \"('createdAt', 'target', 'likeCount', 'collectTime', 'content')\\n\", \"()\\n\"], \"execution_count\": 6}], \"source\": [\"for i in range(len(Repost._fields) // 5 + 1):\\n\", \" print(Repost._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`Question` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 7, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\\n\", \"('pictures', 'liked', 'likeIcon', 'collected', 'updatedAt')\\n\", \"('id', 'targetType', 'status', 'answerCount', 'viewType')\\n\", \"('createdAt', 'target', 'likeCount', 'collectTime', 'title')\\n\", \"('content',)\\n\"], \"execution_count\": 7}], \"source\": [\"for i in range(len(Question._fields) // 5 + 1):\\n\", \" print(Question._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`Answer` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 8, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'richtextContent', 'commentCount', 'read', 'questionId')\\n\", \"('isCommentForbidden', 'type', 'pictures', 'liked', 'voteTend')\\n\", \"('likeIcon', 'collected', 'id', 'targetType', 'status')\\n\", \"('upVoteCount', 'viewType', 'createdAt', 'target', 'likeCount')\\n\", \"('collectTime', 'question', 'content')\\n\"], \"execution_count\": 8}], \"source\": [\"for i in range(len(Answer._fields) // 5 + 1):\\n\", \" print(Answer._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`Comment` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 9, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'replyCount', 'collectedTime')\\n\", \"('likeInfo', 'topic', 'level', 'commentCount', 'enablePictureComments')\\n\", \"('read', 'threadId', 'isCommentForbidden', 'type', 'pictures')\\n\", \"('liked', 'hotReplies', 'targetId', 'likeIcon', 'collected')\\n\", \"('id', 'targetType', 'status', 'viewType', 'createdAt')\\n\", \"('target', 'likeCount', 'collectTime', 'content')\\n\"], \"execution_count\": 9}], \"source\": [\"for i in range(len(Comment._fields) // 5 + 1):\\n\", \" print(Comment._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`PersonalUpdateSection` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 10, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'repostCount', 'user', 'collectedTime', 'likeInfo')\\n\", \"('topic', 'commentCount', 'read', 'isCommentForbidden', 'type')\\n\", \"('pictures', 'liked', 'items', 'likeIcon', 'collected')\\n\", \"('id', 'targetType', 'status', 'viewType', 'createdAt')\\n\", \"('target', 'likeCount', 'collectTime', 'content')\\n\"], \"execution_count\": 10}], \"source\": [\"for i in range(len(PersonalUpdateSection._fields) // 5 + 1):\\n\", \" print(PersonalUpdateSection._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"`PersonalUpdate` \\u7c7b\\u53ef\\u7528\\u7684\\u5c5e\\u6027\"]}, {\"cell_type\": \"code\", \"execution_count\": 11, \"metadata\": {}, \"outputs\": [{\"name\": \"stdout\", \"output_type\": \"stream\", \"text\": [\"('linkInfo', 'topics', 'repostCount', 'user', 'targetUsers')\\n\", \"('collectedTime', 'likeInfo', 'topic', 'topicIds', 'usernames')\\n\", \"('commentCount', 'read', 'actionTime', 'isCommentForbidden', 'type')\\n\", \"('pictures', 'liked', 'users', 'likeIcon', 'collected')\\n\", \"('targetUsernames', 'action', 'id', 'targetType', 'status')\\n\", \"('viewType', 'createdAt', 'target', 'likeCount', 'collectTime')\\n\", \"('updateIds', 'content')\\n\"], \"execution_count\": 11}], \"source\": [\"for i in range(len(PersonalUpdate._fields) // 5 + 1):\\n\", \" print(PersonalUpdate._fields[i*5:(i+1)*5])\"]}, {\"cell_type\": \"markdown\", \"metadata\": {}, \"source\": [\"------\\n\", \"\\n\", \"Prev: \\u7b80\\u5355\\u7684\\u4f8b\\u5b50 \\ud83c\\udf30\\n\", \"Next: \\u5373\\u523bWeb API\"]}], \"metadata\": {\"anaconda-cloud\": {}, \"kernelspec\": {\"display_name\": \"Python [conda env:py3]\", \"language\": \"python\", \"name\": \"conda-env-py3-py\"}, \"language_info\": {\"codemirror_mode\": {\"name\": \"ipython\", \"version\": 3}, \"file_extension\": \".py\", \"mimetype\": \"text/x-python\", \"name\": \"python\", \"nbconvert_exporter\": \"python\", \"pygments_lexer\": \"ipython3\", \"version\": \"3.6.4\"}}, \"nbformat\": 4, \"nbformat_minor\": 2}" }, { "path": "docs/static/custom.css", "content": "/*\nAuthor: Neil Panchal\nhttp://neil.panchal.io\n\nNew colors. These are a bit darker to allow for a better contrast. I started with Solarized palette and adjusted the colors as needed.\n\n\nYELLOW = #ba9600\t\t\t[181,137,0]\nORANGE = #BA5400\t\t\t[186,84,0]\nRED = #D43132\t\t\t[176,47,48]\nMAGENTA = #D14187\t\t\t[209,65,135]\nVIOLET = #793ac7\t\t\t[144,89,212]\nBLUE = #007ad0\t\t\t[15,134,217]\nCYAN = #009489\t\t\t[0,163,151]\nGREEN = #688A0A\t\t\t[104,138,10]\nBROWN \t\t= #A05200\t\t\t[156,98,48]\n\nBACKGROUND = #F8F8F8\nDARK GRAY = #383838\nMID GRAY = #d8d8d8\nLIGHT GRAY = #828282\n*/\n\nhtml {\n font-size: 16px !important;\n}\n\nbody {\n background-color: #FFF !important;\n color: #828282;\n font-weight: normal;\n font-family: \"Helvetica Neue\", Helvetica, Arial, sans-serif;\n overflow: inherit;\n}\n\nbody .notebook-app {\n background-color: #FFF !important;\n}\n\n#header {\n box-shadow: none !important;\n}\n\n#notebook {\n padding-top: 0px;\n font-family: \"Times New Roman\", serif;\n\n}\n\n.container {\n width: 940px;\n}\n\n#site {\n overflow: inherit;\n}\n\n#notebook-container {\n box-shadow: none;\n -webkit-box-shadow: none;\n padding: 0px;\n}\n\n.nav > li > a {\n padding: 10px;\n}\n\ndiv.output_wrapper {\n margin-top: 8px;\n}\n\na {\n color: #383838;\n}\n\ncode,\nkbd,\npre,\nsamp {\n font-family: 'Dejavu Sans Mono', 'Menlo', 'Courier New', monospace !important;\n font-size: 0.8rem !important;\n}\n\nh1 {\n font-family: \"Times New Roman\", serif !important;\n font-size: 1.6rem !important;\n font-weight: 300 !important;\n color: #000000 !important;\n /* letter-spacing: 3px !important; */\n text-transform: normal !important;\n text-align: center !important;\n padding-top: 3rem;\n padding-bottom: 3rem;\n /*border-bottom: 1px dotted #000000 !important;*/\n}\n\nh2 {\n font-family: \"Times New Roman\", serif !important;\n font-size: 1.25rem !important;\n font-weight: bold !important;\n color: #000000 !important;\n text-transform: none !important;\n margin: 2rem 0 0.75rem !important;\n}\n\nh3 {\n font-family: \"Times New Roman\", serif !important;\n font-size: 1.125rem !important;\n font-weight: normal !important;\n font-style: italic !important;\n color: #000000 !important;\n display: block !important;\n margin: 1.25rem 0 0.75rem !important;\n}\n\nh4 {\n font-style: italic;\n text-decoration: underline;\n}\n\nh4,\nh5,\nh6 {\n font-family: \"Times New Roman\", serif !important;\n font-size: 1rem !important;\n font-weight: 500 !important;\n display: block !important;\n color: #000000;\n margin: 0.5rem 0 0.25rem !important;\n\n}\n\n.prompt {\n font-family: 'Menlo', 'Courier New', monospace !important;\n font-style: normal;\n font-size: 0.75rem;\n text-align: right;\n line-height: 1rem;\n}\n\n/* INTRO PAGE */\n\n.toolbar_info,\n.list-container {\n color: #828282;\n}\n\n/* NOTEBOOK */\n\ndiv#header-container {\n /* display: none !important; */\n}\n\ndiv.nbp-app {\n display: none !important;\n}\n\ndiv#notebook {\n border-top: none;\n font-size: 1rem;\n}\n\n.kernel_idle_icon:before {\n color: #D14187;\n}\n\n.kernel_busy_icon:before {\n color: #009489;\n}\n\ndiv.input_prompt {\n color: #888;\n font-style: normal !important;\n}\n\ndiv.input_prompt bdi, div.output_prompt bdi {\n line-height: 0;\n font-size: 0;\n color: transparent;\n /*left: -10000px;*/\n /*content: \"\\21E2\";*/\n}\n\n/* Unicode Arrows\nSource: http://www.copypastecharacter.com/\n\n▼ ↪ ↩ ← ↑ → ↓ ↔ ↕ ↖ ↗ ↘ ↙ ↚ ↛ ↜ ↝ ↞ ↟ ↠ ↡ ↢ ↣ ↤ ↦ ↥ ↧ ↨ ↫ ↬ ↭ ↮ ↯ ↰ ↱ ↲ ↴ ↳ ↵ ↶ ↷ ↸ ↹ ↺ ↻ ⟲ ⟳ ↼ ↽ ↾ ↿ ⇀ ⇁ ⇂ ⇃ ⇄ ⇅ ⇆ ⇇ ⇈ ⇉ ⇊ ⇋ ⇌ ⇍ ⇏ ⇎ ⇑ ⇓ ⇐ ⇒ ⇔ ⇕ ⇖ ⇗ ⇘ ⇙ ⇳ ⇚ ⇛ ⇜ ⇝ ⇞ ⇟ ⇠ ⇡ ⇢ ⇣ ⇤ ⇥ ⇦ ⇨ ⇩ ⇪ ⇧ ⇫ ⇬ ⇭ ⇮ ⇯ ⇰ ⇱ ⇲ ⇴ ⇵ ⇶ ⇷ ⇸ ⇹ ⇺ ⇻ ⇼ ⇽ ⇾ ⇿ ⟰ ⟱ ⟴ ⟵ ⟶ ⟷ ⟸ ⟹ ⟽ ⟾ ⟺ ⟻ ⟼ ⟿ ⤀ ⤁ ⤅ ⤂ ⤃ ⤄ ⤆ ⤇ ⤈ ⤉ ⤊ ⤋ ⤌ ⤍ ⤎ ⤏ ⤐ ⤑ ⤒ ⤓ ⤔ ⤕ ⤖ ⤗ ⤘ ⤙ ⤚ ⤛ ⤜ ⤝ ⤞ ⤟ ⤠ ⤡ ⤢ ⤣ ⤤ ⤥ ⤦ ⤧ ⤨ ⤩ ⤪ ⤭ ⤮ ⤯ ⤰ ⤱ ⤲ ⤳ ⤻ ⤸ ⤾ ⤿ ⤺ ⤼ ⤽ ⤴ ⤵ ⤶ ⤷ ⤹ ⥀ ⥁ ⥂ ⥃ ⥄ ⥅ ⥆ ⥇ ⥈ ⥉ ⥒ ⥓ ⥔ ⥕ ⥖ ⥗ ⥘ ⥙ ⥚ ⥛ ⥜ ⥝ ⥞ ⥟ ⥠ ⥡ ⥢ ⥣ ⥤ ⥥ ⥦ ⥧ ⥨ ⥩ ⥪ ⥫ ⥬ ⥭ ⥮ ⥯ ⥰ ⥱ ⥲ ⥳ ⥴ ⥵ ⥶ ⥷ ⥸ ⥹ ⥺ ⥻ ➔ ➘ ➙ ➚ ➛ ➜ ➝ ➞ ➟ ➠ ➡ ➢ ➣ ➤ ➥ ➦ ➧ ➨ ➩ ➪ ➫ ➬ ➭ ➮ ➯ ➱ ➲ ➳ ➴ ➵ ➶ ➷ ➸ ➹ ➺ ➻ ➼ ➽ ➾ ⬀ ⬁ ⬂ ⬃ ⬄ ⬅ ⬆ ⬇ ⬈ ⬉ ⬊ ⬋ ⬌ ⬍ ⏎ ▲ ▼ ◀ ▶ ⬎ ⬏ ⬐ ⬑ ☇ ☈ ⍃ ⍄ ⍇ ⍈ ⍐ ⍗ ⍌ ⍓ ⍍ ⍔ ⍏ ⍖ ⍅ ⍆\n\n*/\ndiv.output_prompt {\n color: #000000;\n}\n\n.code_cell div.input_prompt:after,\n.code_cell div.output_prompt:after {\n display: inline-block;\n content: '';\n font-size: 0.75rem;\n font-style: normal !important;\n}\n\ndiv.input_area {\n border-radius: 0px;\n border: none;\n padding: 0px 5px;\n}\n\ndiv.output_area pre {\n font-weight: normal;\n color: #828282;\n}\n\ndiv.output_subarea {\n font-weight: normal;\n color: #828282;\n}\n\n.rendered_html pre,\n.rendered_html code {\n color: #828282;\n}\n\ndiv.output_html {\n font-size: 0.75rem;\n font-weight: normal;\n font-family: 'Menlo', 'Courier New', monospace;\n}\n\ntable.dataframe {\n border-collapse: collapse;\n border: none;\n}\n\ntable.dataframe thead {\n padding-bottom: 10px;\n}\n\ntable.dataframe thead tr {\n color: #A05200;\n font-style: normal;\n padding: 5px 10px;\n border-bottom: 1px solid #828282;\n vertical-align: middle;\n text-align: center;\n}\n\ntable.dataframe thead tr th, table.dataframe thead tr:only-child th {\n vertical-align: middle;\n text-align: center;\n}\n\ntable.dataframe tbody {\n padding-top: 5px;\n}\n\ntable.dataframe tbody tr {\n\n}\n\ntable.dataframe tbody tr th {\n background-color: #ffffff;\n text-align: left;\n font-style: italic\n}\n\ntable.dataframe tbody tr td {\n background-color: #fff9ea;\n color: #828282;\n padding-left: 1.0rem;\n padding-right: 1.0rem;\n}\n\n.table > thead > tr > td.info,\n.table > tbody > tr > td.info,\n.table > tfoot > tr > td.info,\n.table > thead > tr > th.info,\n.table > tbody > tr > th.info,\n.table > tfoot > tr > th.info,\n.table > thead > tr.info > td,\n.table > tbody > tr.info > td,\n.table > tfoot > tr.info > td,\n.table > thead > tr.info > th,\n.table > tbody > tr.info > th,\n.table > tfoot > tr.info > th {\n background-color: #d9edf7;\n}\n\ndiv.cell {\n padding: 12px 0px 5px 0px;\n right: 2px;\n border: none;\n}\n\ndiv.cell.selected {\n border-radius: 0px;\n}\n\ndiv.cell.selected {\n border: none;\n background: none !important;\n}\n\ndiv.cell.selected .inner_cell {\n border-right: 2px solid #CCC;\n margin-right: -2px;\n}\n\ndiv.cell.selected div.input:before {\n display: inline-block;\n position: absolute;\n content: \"\\279E\";\n font-size: 0.875rem;\n top: 16px;\n width: 10px;\n left: 30px;\n color: #009489;\n font-style: bold !important;\n}\n\n.edit_mode div.cell.selected div.input:before {\n color: #D14187;\n}\n\ndiv.cell.selected:before, div.cell.selected.jupyter-soft-selected:before {\n position: absolute;\n display: none;\n top: -1px;\n left: -1px;\n width: 0px;\n height: calc(100% + 2px);\n content: '';\n background: none;\n}\n\ndiv.out_prompt_overlay:hover {\n box-shadow: none;\n background: none;\n}\n\n.edit_mode div.cell.selected {\n border: none;\n background: none !important;\n}\n\n.edit_mode div.cell.selected:before {\n width: 0px;\n background: none;\n}\n\ndiv.cell.edit_mode {\n border-radius: 0px;\n border: thin solid #BA5400;\n}\n\ndiv.text_cell_render,\ndiv.output_html {\n color: #333;\n}\n\nspan.ansiblack {\n color: #828282;\n}\n\nspan.ansiblue {\n color: #009489;\n}\n\nspan.ansigray {\n color: #d8d8d8;\n}\n\nspan.ansigreen {\n color: #688A0A;\n}\n\nspan.ansipurple {\n color: #793ac7;\n}\n\nspan.ansired {\n color: #D43132;\n}\n\nspan.ansiyellow {\n color: #ba9600;\n}\n\ndiv.output_stderr {\n background-color: #FFFFFF;\n}\n\ndiv.output_stderr pre {\n color: #793ac7;\n}\n\n.cm-s-ipython.CodeMirror {\n background: #F8F8F8;\n color: #828282;\n}\n\n.cm-s-ipython div.CodeMirror-selected {\n background: #e8e8e8 !important;\n}\n\n.cm-s-ipython .CodeMirror-gutters {\n background: #F8F8F8;\n border-right: 0px;\n}\n\n.cm-s-ipython .CodeMirror-linenumber {\n color: #b8b8b8;\n}\n\n.cm-s-ipython .CodeMirror-cursor {\n border-left: 1px solid #585858 !important;\n}\n\n.cm-s-ipython span.cm-comment {\n color: #828282;\n}\n\n.cm-s-ipython span.cm-atom {\n color: #D14187;\n}\n\n.cm-s-ipython span.cm-number {\n color: #007ad0;\n}\n\n.cm-s-ipython span.cm-property {\n color: #D14187;\n}\n\n.cm-s-ipython span.cm-attribute {\n color: #688A0A;\n}\n\n.cm-s-ipython span.cm-keyword {\n font-weight: normal;\n color: #D43132;\n}\n\n.cm-s-ipython span.cm-string {\n color: #ba9600;\n}\n\n.cm-s-ipython span.cm-operator {\n color: #BA5400;\n font-weight: normal;\n}\n\n.cm-s-ipython span.cm-builtin {\n color: #793ac7;\n}\n\n.cm-s-ipython span.cm-variable {\n color: #009489;\n}\n\n.cm-s-ipython span.cm-variable-2 {\n color: #007ad0;\n}\n\n.cm-s-ipython span.cm-def {\n color: #009489;\n}\n\n.cm-s-ipython span.cm-error {\n /*background: #FFBDBD;*/\n color: #D43132;\n}\n\n.cm-s-ipython span.cm-bracket {\n color: #828282;\n}\n\n.cm-s-ipython span.cm-tag {\n color: #D43132;\n}\n\n.cm-s-ipython span.cm-link {\n color: #793ac7;\n}\n\n.cm-s-ipython .CodeMirror-matchingbracket {\n text-decoration: underline;\n color: #828282 !important;\n}\n\n/* ANSI colors */\n\n.ansi-red-fg {\n color: #D43132;\n font-weight: bold;\n}\n\n.ansi-green-fg {\n color: #688A0A;\n}\n\n.ansi-cyan-fg {\n color: #009489;\n}\n\n.ansi-blue-fg {\n color: #007ad0;\n}\n" }, { "path": "docs/templates/full.tpl", "content": "{%- extends 'basic.tpl' -%}\n\n{%- block header -%}\n\n\n\n{%- block html_head -%}\n\n{{resources['metadata']['name']}}\n\n{%- if \"widgets\" in nb.metadata -%}\n\n{%- endif-%}\n\n\n\n\n\n\n\n\n\n\n\n\n{%- endblock html_head -%}\n\n{%- endblock header -%}\n\n{% block body %}\n\n
\n
\n{{ super() }}\n
\n
\n\n{%- endblock body %}\n\n{% block footer %}\n{{ super() }}\n\n{% endblock footer %}\n" }, { "path": "jike/__init__.py", "content": "# -*- coding: utf-8 -*-\n\n# ___ __ __ ___ __\n# / (_) /_____ / |/ /__ / /__________\n# __ / / / //_/ _ \\ / /|_/ / _ \\/ __/ ___/ __ \\\n# / /_/ / / ,< / __/ / / / / __/ /_/ / / /_/ /\n# \\____/_/_/|_|\\___/ /_/ /_/\\___/\\__/_/ \\____/\n#\n\n\"\"\"\nJike Client\n~~~~~~~~~~~\n\n:copyright: (c) 2018 by Soros Liu.\n:license: MIT, see LICENSE for more details.\n\"\"\"\n\nfrom .client import JikeClient\n\n__title__ = 'jike'\n__version__ = '0.5.0'\n__description__ = 'Jike Metro 🚇 : Jike Python SDK'\n__url__ = 'https://github.com/Sorosliu1029/Jike-Metro'\n__author__ = 'Soros Liu'\n__author_email__ = 'soros.liu1029@gmail.com'\n__license__ = 'MIT'\n__copyright__ = 'Copyright 2018 Soros Liu'\n__keywords__ = 'jike crawl tool'\n" }, { "path": "jike/__main__.py", "content": "# -*- coding: utf-8 -*-\n\nimport sys\nfrom .client import JikeClient\n\noptions = sys.argv[1:]\nif not {'news', 'follow'} & set(options):\n print('Please provide a notification option: \"news\" or \"follow\"')\n sys.exit(0)\n\nc = JikeClient(sync_unread=True)\nif 'news' in options:\n c.get_news_feed()\n print('Jike Metro 🚇 will notify you when your subscribed topics update. 🐈 ')\nif 'follow' in options:\n c.get_following_update()\n print('Jike Metro 🚇 will notify you when your following users update. 🐈 ')\n" }, { "path": "jike/client.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nClient that Jikers play with\n\"\"\"\n\nimport webbrowser\nfrom threading import Timer\nfrom .session import JikeSession\nfrom .objects import List, Stream, User, Topic, JikeEmitter\nfrom .utils import *\nfrom .constants import ENDPOINTS, URL_VALIDATION_PATTERN, CHECK_UNREAD_COUNT_PERIOD\n\n\ndef check_unread_count_periodically(obj):\n \"\"\"\n Run periodical task to check unread count and do some automatic task\n \"\"\"\n obj.get_news_feed_unread_count()\n unread = auto_load_unread(obj)\n notify_update(obj, unread)\n Timer(\n CHECK_UNREAD_COUNT_PERIOD,\n check_unread_count_periodically,\n args=(obj,)\n ).start()\n\n\ndef auto_load_unread(obj):\n unread_news_feed = obj._load_unread('news_feed')\n unread_following_update = obj._load_unread('following_update')\n return unread_news_feed, unread_following_update\n\n\ndef notify_update(obj, unread):\n for t in unread:\n for message in t:\n assert hasattr(message, 'type') and hasattr(message, 'content')\n if message.type == 'OFFICIAL_MESSAGE' and (message.topic['content'] in obj.notified_topics or 'all' in obj.notified_topics):\n title = '{} 更新了'.format(message.topic['content'])\n msg = message.content\n notify(title, msg)\n elif message.type == 'ORIGINAL_POST' and (message.user['screenName'] in obj.notified_users or 'all' in obj.notified_users):\n title = '{} 发动态了'.format(message.user['screenName'])\n msg = message.content\n notify(title, msg)\n\n\nclass JikeClient:\n def __init__(self, sync_unread=False):\n self.auth_token = read_token()\n if self.auth_token is None:\n self.auth_token = login()\n try:\n write_token(self.auth_token)\n except IOError:\n pass\n self.jike_session = JikeSession(self.auth_token)\n\n self.collection = None\n self.news_feed = None\n self.following_update = None\n\n self.unread_count = 0\n self.timer = None\n if sync_unread:\n self.timer = Timer(\n CHECK_UNREAD_COUNT_PERIOD,\n check_unread_count_periodically,\n args=(self,)\n ).start()\n\n self.notified_topics = ['all']\n self.notified_users = ['all']\n\n def __del__(self):\n if self.timer:\n self.timer.cancel()\n\n def get_my_profile(self):\n return self.get_user_profile(username=None)\n\n def get_my_collection(self):\n if self.collection is None:\n self.collection = List(self.jike_session, ENDPOINTS['my_collections'])\n self.collection.load_more()\n return self.collection\n\n def get_news_feed_unread_count(self):\n res = self.jike_session.get(ENDPOINTS['news_feed_unread_count'])\n if res.status_code == 200:\n result = res.json()\n self.unread_count = result['newMessageCount']\n return self.unread_count\n res.raise_for_status()\n\n def get_news_feed(self):\n if self.news_feed is None:\n self.news_feed = Stream(self.jike_session, ENDPOINTS['news_feed'])\n self.news_feed.load_more()\n return self.news_feed\n\n def get_following_update(self):\n if self.following_update is None:\n self.following_update = Stream(self.jike_session, ENDPOINTS['following_update'])\n self.following_update.load_more()\n return self.following_update\n\n def get_user_profile(self, username):\n res = self.jike_session.get(ENDPOINTS['user_profile'], {\n 'username': username\n })\n if res.status_code == 200:\n result = res.json()\n result['user'].update(result['statsCount'])\n return User(**result['user'])\n res.raise_for_status()\n\n def get_user_post(self, username, limit=20):\n posts = List(self.jike_session, ENDPOINTS['user_post'], {'username': username})\n posts.load_more(limit)\n return posts\n\n def get_user_created_topic(self, username, limit=20):\n created_topics = List(self.jike_session, ENDPOINTS['user_created_topic'], {'username': username}, Topic)\n created_topics.load_more(limit)\n return created_topics\n\n def get_user_subscribed_topic(self, username, limit=20):\n subscribed_topics = List(self.jike_session, ENDPOINTS['user_subscribed_topic'], {'username': username}, Topic)\n subscribed_topics.load_more(limit)\n return subscribed_topics\n\n def get_user_following(self, username, limit=20):\n user_followings = List(self.jike_session, ENDPOINTS['user_following'], {'username': username}, User)\n user_followings.load_more(limit)\n return user_followings\n\n def get_user_follower(self, username, limit=20):\n user_followers = List(self.jike_session, ENDPOINTS['user_follower'], {'username': username}, User)\n user_followers.load_more(limit)\n return user_followers\n\n def get_comment(self, message):\n assert hasattr(message, 'id') and hasattr(message, 'type')\n comments = Stream(self.jike_session, ENDPOINTS['list_comment'], {\n 'targetId': message.id,\n 'targetType': message.type\n })\n comments.load_more()\n return comments\n\n def get_topic_selected(self, topic_id):\n topic_selected = Stream(self.jike_session, ENDPOINTS['topic_selected'], {\n 'topic': topic_id\n })\n topic_selected.load_more()\n return topic_selected\n\n def get_topic_square(self, topic_id):\n topic_square = Stream(self.jike_session, ENDPOINTS['topic_square'], {\n 'topicId': topic_id\n })\n topic_square.load_more()\n return topic_square\n\n @staticmethod\n def open_in_browser(url_or_message):\n if isinstance(url_or_message, str):\n url = url_or_message\n elif hasattr(url_or_message, 'linkInfo'):\n url = url_or_message.linkInfo['linkUrl']\n elif 'linkInfo' in url_or_message:\n url = url_or_message['linkInfo']['linkUrl']\n elif hasattr(url_or_message, 'content'):\n urls = extract_url(url_or_message.content)\n if urls:\n for url in urls:\n webbrowser.open(url)\n return\n else:\n raise ValueError('No url found')\n\n if not URL_VALIDATION_PATTERN.match(url):\n raise ValueError('Url invalid')\n else:\n webbrowser.open(url)\n\n def create_my_post(self, content, link=None, topic_id=None, pictures=None):\n assert isinstance(content, str)\n if link and pictures:\n raise ValueError('Jike cannot post thought with both pictures and link')\n\n payload = {\n 'content': content\n }\n if link:\n assert URL_VALIDATION_PATTERN.match(link), 'Invalid link'\n payload.update({'linkInfo': extract_link(self.jike_session, link)})\n if topic_id:\n payload.update({'submitToTopic': topic_id})\n if pictures:\n uploaded_picture_keys = upload_pictures(pictures)\n payload.update({'pictureKeys': uploaded_picture_keys})\n\n res = self.jike_session.post(ENDPOINTS['create_post'], json=payload)\n post = None\n if res.status_code == 200:\n result = res.json()\n if result['success']:\n post = OriginalPost(**result['data'])\n else:\n raise RuntimeError('Post fail')\n res.raise_for_status()\n return post\n\n def delete_my_post(self, post):\n assert hasattr(post, 'type') and hasattr(post, 'id')\n res = self.jike_session.post(ENDPOINTS['delete_post'], json={\n 'id': post.id,\n })\n if res.status_code == 200:\n return res.json()['success']\n res.raise_for_status()\n\n def _like_action(self, message, action):\n assert hasattr(message, 'type') and hasattr(message, 'id')\n assert message.type in converter, 'Unsupported message type'\n assert action in ['like_it', 'unlike_it']\n message_type = ''.join([w.title() if i != 0 else w.lower()\n for i, w in enumerate(message.type.split('_'))]) + 's'\n endpoint = ENDPOINTS[action].format(t=message_type)\n payload = {\n 'id': message.id,\n }\n if hasattr(message, 'targetType'):\n payload.update({'targetType': message.targetType})\n res = self.jike_session.post(endpoint, json=payload)\n if res.status_code == 200:\n return res.json()['success']\n res.raise_for_status()\n\n def like_it(self, message):\n return self._like_action(message, 'like_it')\n\n def unlike_it(self, message):\n return self._like_action(message, 'unlike_it')\n\n def _collect_action(self, message, action):\n assert hasattr(message, 'type') and hasattr(message, 'id')\n assert message.type in converter, 'Unsupported message type'\n assert action in ['collect_it', 'uncollect_it']\n message_type = ''.join([w.title() if i != 0 else w.lower()\n for i, w in enumerate(message.type.split('_'))]) + 's'\n endpoint = ENDPOINTS[action].format(t=message_type)\n payload = {\n 'id': message.id,\n }\n res = self.jike_session.post(endpoint, json=payload)\n if res.status_code == 200:\n return res.json()['success']\n res.raise_for_status()\n\n def collect_it(self, message):\n return self._collect_action(message, 'collect_it')\n\n def uncollect_it(self, message):\n return self._collect_action(message, 'uncollect_it')\n\n def repost_it(self, content, message, sync_comment=True):\n assert isinstance(content, str)\n assert hasattr(message, 'type') and hasattr(message, 'id')\n assert message.type in converter, 'Unsupported message type'\n payload = {\n 'content': content,\n 'syncComment': sync_comment,\n 'targetId': message.id,\n 'targetType': message.type,\n }\n res = self.jike_session.post(ENDPOINTS['repost_it'], json=payload)\n repost = None\n if res.status_code == 200:\n result = res.json()\n if result['success']:\n repost = Repost(**result['data'])\n else:\n raise RuntimeError('Repost fail')\n res.raise_for_status()\n return repost\n\n def comment_it(self, content, message, pictures=None, sync2personal_updates=True):\n assert isinstance(content, str)\n assert hasattr(message, 'type') and hasattr(message, 'id')\n assert message.type in converter, 'Unsupported message type'\n payload = {\n 'content': content,\n 'pictureKeys': [],\n 'syncToPersonalUpdates': sync2personal_updates,\n 'targetId': message.id,\n 'targetType': message.type,\n }\n if pictures:\n uploaded_picture_keys = upload_pictures(pictures)\n payload.update({'pictureKeys': uploaded_picture_keys})\n res = self.jike_session.post(ENDPOINTS['comment_it'], json=payload)\n comment = None\n if res.status_code == 200:\n result = res.json()\n if result['success']:\n comment = Comment(**result['data'])\n else:\n raise RuntimeError('Comment fail')\n res.raise_for_status()\n return comment\n\n def search_topic(self, keywords):\n assert isinstance(keywords, str)\n topics = List(self.jike_session, ENDPOINTS['search_topic'], type_converter=Topic, fixed_extra_payload={\n 'keywords': keywords,\n 'onlyUserPostEnabled': False,\n 'type': 'ALL'\n })\n topics.load_more()\n return topics\n\n def search_collection(self, keywords):\n assert isinstance(keywords, str)\n messages = List(self.jike_session, ENDPOINTS['search_collection'], fixed_extra_payload={\n 'keywords': keywords,\n })\n messages.load_more()\n return messages\n\n def get_recommended_topic(self):\n topics = List(self.jike_session, ENDPOINTS['recommended_topic'], type_converter=Topic, fixed_extra_payload={\n 'categoryAlias': 'RECOMMENDATION',\n })\n topics.load_more()\n return topics\n\n def create_emitter(self, endpoint, fixed_extra_payload=()):\n \"\"\"\n BOOM! You find easter egg in this project, now you can use this function to crawl Jike.\n\n USE IT WISELY !\n \"\"\"\n assert endpoint in ENDPOINTS.values()\n return JikeEmitter(self.jike_session, endpoint, fixed_extra_payload)\n\n def schedule_my_post(self, content, link=None, topic_id=None, pictures=None, *, delay=None):\n assert isinstance(delay, int) and delay > 0, 'Please provide a delay time'\n post_fn = self.create_my_post\n timer = Timer(delay, post_fn, args=(content, link, topic_id, pictures))\n timer.start()\n return timer\n\n def _load_unread(self, choice):\n if choice == 'news_feed':\n if self.news_feed:\n return self.news_feed.load_update(unread_count=self.unread_count+3)\n elif choice == 'following_update':\n if self.following_update:\n return self.following_update.load_update(unread_count=self.unread_count+3)\n else:\n raise ValueError('choice only can be \"news_feed\" or \"following_update\"')\n return []\n\n def set_automatic_rules(self, notified_topics, notified_users):\n self.notified_topics = notified_topics\n self.notified_users = notified_users\n\n def _create_new_jike_session(self):\n \"\"\"\n Create a new session of `requests.Session`\n\n CAUTION: Could be used for concurrency http request, but not tested and verified by author\n \"\"\"\n return JikeSession(self.auth_token)\n\n def relogin(self):\n \"\"\"\n Re-login in case any problem related to auth_token\n \"\"\"\n self.auth_token = login()\n write_token(self.auth_token)\n self.jike_session.session.close()\n self.jike_session = JikeSession(self.auth_token)\n" }, { "path": "jike/constants.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nThis module provides constants for Jike.\n\"\"\"\n\nimport re\nimport os\nfrom string import Template\n\nJIKE_URI_SCHEME_FMT = 'jike://page.jk/web?url=https%3A%2F%2Fruguoapp.com%2Faccount%2Fscan%3Fuuid%3D{uuid}&displayHeader=false&displayFooter=false'\n\nAUTH_TOKEN_STORE_PATH = os.path.join(os.path.expanduser('~'), '.local', 'jike', 'jike_metro.json')\nif not os.path.exists(os.path.dirname(AUTH_TOKEN_STORE_PATH)):\n os.makedirs(os.path.dirname(AUTH_TOKEN_STORE_PATH))\n\nSTREAM_CAPACITY_LIMIT = 1000\n\nCHECK_UNREAD_COUNT_PERIOD = 60 * 3\n\nURL_VALIDATION_PATTERN = re.compile(\n r'(?:http|ftp)s?://' # http:// or https://\n r'(?:(?:[A-Z0-9](?:[A-Z0-9-]{0,61}[A-Z0-9])?\\.)+(?:[A-Z]{2,6}\\.?|[A-Z0-9-]{2,}\\.?)|' # domain...\n r'localhost|' # localhost...\n r'\\d{1,3}\\.\\d{1,3}\\.\\d{1,3}\\.\\d{1,3})' # ...or ip\n r'(?::\\d+)?' # optional port\n r'(?:[/?]\\S*)', re.IGNORECASE)\n\nRENDER2BROWSER_HTML_TEMPLATE = Template(\"\"\"\n\n\n\n \n Jike Metro\n \n\n\n
\n
Scan for 🚇 🎟️
\n ${qrcode_svg}\n
\n
🚧 with 🐈 by 👷 挖地道的
\n
GitHub: Jike Metro
\n
Code Reviews, Feedbacks and Contributions are warmly welcome.
\n
\n Open an issue or\n Leave a comment\n
\n
\n
\n\n\n\"\"\")\n\nHEADERS = {\n 'Origin': 'http://web.okjike.com',\n 'Referer': 'http://web.okjike.com',\n 'User-Agent': 'Jike Metro',\n 'Accept': 'application/json',\n 'Accept-Encoding': 'gzip, deflate, br',\n 'Accept-Language': 'zh-CN,zh;q=0.9,en-US;q=0.8,en;q=0.7',\n\n 'x-jike-app-auth-jwt': None,\n 'App-Version': '4.1.0',\n 'DNT': '1',\n 'platform': 'web',\n}\n\n_f_ = {\n '_s_': 'https',\n '_d_': 'app.jike.ruguoapp.com',\n '_v_': '1.0',\n '_p_': '{t}'\n}\nENDPOINTS = {\n # login\n 'create_session': '{_s_}://{_d_}/sessions.create'.format(**_f_),\n 'wait_login': '{_s_}://{_d_}/sessions.wait_for_login'.format(**_f_),\n 'confirm_login': '{_s_}://{_d_}/sessions.wait_for_confirmation'.format(**_f_),\n # myself\n 'my_collections': '{_s_}://{_d_}/{_v_}/users/collections/list'.format(**_f_),\n # main page info stream\n 'news_feed': '{_s_}://{_d_}/{_v_}/newsFeed/list'.format(**_f_),\n 'news_feed_unread_count': '{_s_}://{_d_}//{_v_}/newsFeed/countUnreads'.format(**_f_),\n 'following_update': '{_s_}://{_d_}/{_v_}/personalUpdate/followingUpdates'.format(**_f_),\n # user\n 'user_profile': '{_s_}://{_d_}/{_v_}/users/profile'.format(**_f_),\n 'user_post': '{_s_}://{_d_}/{_v_}/personalUpdate/single'.format(**_f_),\n 'user_created_topic': '{_s_}://{_d_}/{_v_}/customTopics/custom/listCreated'.format(**_f_),\n 'user_subscribed_topic': '{_s_}://{_d_}/{_v_}/users/topics/listSubscribed'.format(**_f_),\n 'user_following': '{_s_}://{_d_}/{_v_}/userRelation/getFollowingList'.format(**_f_),\n 'user_follower': '{_s_}://{_d_}/{_v_}/userRelation/getFollowerList'.format(**_f_),\n # topic\n 'topic_selected': '{_s_}://{_d_}/{_v_}/messages/history'.format(**_f_),\n 'topic_square': '{_s_}://{_d_}/{_v_}/squarePosts/list'.format(**_f_),\n # comment\n 'list_comment': '{_s_}://{_d_}/{_v_}/comments/listPrimary'.format(**_f_),\n # creation\n 'create_post': '{_s_}://{_d_}/{_v_}/originalPosts/create'.format(**_f_),\n 'delete_post': '{_s_}://{_d_}/{_v_}/originalPosts/remove'.format(**_f_),\n 'extract_link': '{_s_}://{_d_}/{_v_}/readability/extract'.format(**_f_),\n 'picture_uptoken': 'https://upload.jike.ruguoapp.com/token',\n 'picture_upload': 'https://up.qbox.me/',\n # interaction\n 'like_it': '{_s_}://{_d_}/{_v_}/{_p_}/like'.format(**_f_),\n 'unlike_it': '{_s_}://{_d_}/{_v_}/{_p_}/unlike'.format(**_f_),\n 'collect_it': '{_s_}://{_d_}/{_v_}/{_p_}/collect'.format(**_f_),\n 'uncollect_it': '{_s_}://{_d_}/{_v_}/{_p_}/uncollect'.format(**_f_),\n 'repost_it': '{_s_}://{_d_}/{_v_}/reposts/add'.format(**_f_),\n 'comment_it': '{_s_}://{_d_}/{_v_}/comments/add'.format(**_f_),\n # search\n 'search_topic': '{_s_}://{_d_}/{_v_}/users/topics/search'.format(**_f_),\n 'search_collection': '{_s_}://{_d_}/{_v_}/users/collections/search'.format(**_f_),\n # recommend\n 'recommended_topic': '{_s_}://{_d_}/{_v_}/topics/recommendation/list'.format(**_f_),\n}\n" }, { "path": "jike/objects/__init__.py", "content": "# -*- coding: utf-8 -*-\n\nfrom .base import List, Stream, JikeEmitter\nfrom .message import *\nfrom .user import User\nfrom .topic import Topic\n" }, { "path": "jike/objects/base.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nSpecial class designed for Jike Metro\n\"\"\"\n\nfrom collections import deque\nfrom collections.abc import Iterable, Sequence\nfrom ..utils import converter\nfrom ..constants import STREAM_CAPACITY_LIMIT\n\n\nclass JikeSequenceBase(Sequence):\n \"\"\"\n Base class for sequence data structure\n\n Has no size limit\n\n Intended for\n - Jike Collection\n - Jike User Post\n - Jike User Created Topic\n - Jike User Subscribed Topic\n - Jike User Following\n - Jike User Follower\n \"\"\"\n\n def __init__(self):\n self.seq = []\n\n def __repr__(self):\n return 'JikeSequenceBase({} items)'.format(len(self.seq))\n\n def __getitem__(self, item):\n return self.seq[item]\n\n def __contains__(self, item):\n return any((item.id == ele.id for ele in self.seq))\n\n def __len__(self):\n return len(self.seq)\n\n def __reversed__(self):\n return reversed(self.seq)\n\n def index(self, item, start=0, stop=None):\n assert hasattr(item, 'id')\n for idx, ele in enumerate(self.seq[start:stop]):\n if ele.id == item.id:\n return idx\n raise ValueError('Item with id: {} not found'.format(item.id))\n\n def append(self, item):\n self.seq.append(item)\n\n def clear(self):\n self.seq.clear()\n\n def extend(self, items):\n assert isinstance(items, Iterable)\n self.seq.extend(list(items))\n\n\nclass JikeStreamBase:\n \"\"\"\n Base class for stream data structure\n\n Has size limit specified by `maxlen`, default is 200\n\n Intended for\n - Jike News Feed\n - Jike Following Update\n - Jike Comment\n - Jike Topic Selected\n - Jike Topic Square\n \"\"\"\n\n def __init__(self, maxlen=200):\n self.queue = deque(maxlen=maxlen)\n\n def __repr__(self):\n return 'JikeStreamBase({} items)'.format(len(self.queue))\n\n def __getitem__(self, item):\n return self.queue[item]\n\n def __contains__(self, item):\n return any((item.id == ele.id for ele in self.queue))\n\n def __len__(self):\n return len(self.queue)\n\n def __reversed__(self):\n return reversed(self.queue)\n\n def index(self, item, start=0, stop=None):\n assert hasattr(item, 'id')\n for idx, ele in enumerate(list(self.queue)[start:stop]):\n if ele.id == item.id:\n return idx\n raise ValueError('Item with id: {} not found'.format(item.id))\n\n def append(self, item):\n self.queue.append(item)\n\n def appendleft(self, item):\n self.queue.appendleft(item)\n\n def clear(self):\n self.queue.clear()\n\n def extend(self, items):\n assert isinstance(items, Iterable)\n self.queue.extend(items)\n\n def extendleft(self, items):\n assert isinstance(items, Iterable)\n self.queue.extendleft(items)\n\n def pop(self):\n return self.queue.pop()\n\n def popleft(self):\n return self.queue.popleft()\n\n\nclass JikeFetcher:\n \"\"\"\n Used to fetch Jike content in json format\n \"\"\"\n\n def __init__(self, jike_session):\n self.jike_session = jike_session\n self.load_more_key = None\n\n def __repr__(self):\n return 'JikeFetcher({})'.format(repr(self.jike_session))\n\n def fetch_more(self, endpoint, payload):\n res = self.jike_session.post(endpoint, json=payload)\n if res.status_code == 200:\n return res.json()\n res.raise_for_status()\n\n\nclass List(JikeSequenceBase, JikeFetcher):\n \"\"\"\n Object type for Collections / Posts / Topics / Followings / Followers\n \"\"\"\n\n def __init__(self, jike_session, endpoint, fixed_extra_payload=(), type_converter=None):\n super().__init__()\n JikeFetcher.__init__(self, jike_session)\n self.endpoint = endpoint\n self.fixed_extra_payload = dict(fixed_extra_payload)\n self.converter = type_converter\n\n def __repr__(self):\n return 'List({} items)'.format(len(self.seq))\n\n def load_more(self, limit=20, extra_payload=()):\n payload = {\n 'limit': limit,\n 'loadMoreKey': self.load_more_key,\n }\n payload.update(self.fixed_extra_payload)\n payload.update(dict(extra_payload))\n result = super().fetch_more(self.endpoint, payload)\n try:\n self.load_more_key = result['loadMoreKey']\n except KeyError:\n self.load_more_key = None\n if self.converter:\n more = [self.converter(**item) for item in result['data']]\n else:\n more = [converter[item['type']](**item) for item in result['data']]\n self.extend(more)\n return more\n\n def load_all(self, extra_payload=()):\n self.load_more(100, extra_payload)\n while self.load_more_key is not None:\n self.load_more(100, extra_payload)\n return len(self.seq)\n\n\nclass Stream(JikeStreamBase, JikeFetcher):\n \"\"\"\n Object type for news feed\n \"\"\"\n\n def __init__(self, jike_session, endpoint, fixed_extra_payload=(), maxlen=200):\n maxlen = min(maxlen, STREAM_CAPACITY_LIMIT)\n super().__init__(maxlen)\n JikeFetcher.__init__(self, jike_session)\n self.endpoint = endpoint\n self.fixed_extra_payload = dict(fixed_extra_payload)\n\n def __repr__(self):\n return 'Stream({} items, with {} capacity)'.format(len(self.queue), self.queue.maxlen)\n\n def load_more(self, limit=20, extra_payload=()):\n payload = {\n 'trigger': 'user',\n 'limit': limit,\n 'loadMoreKey': self.load_more_key,\n }\n payload.update(self.fixed_extra_payload)\n payload.update(dict(extra_payload))\n result = super().fetch_more(self.endpoint, payload)\n try:\n self.load_more_key = result['loadMoreKey']\n except KeyError:\n self.load_more_key = None\n more = [converter[item['type']](**item) for item in result['data']]\n self.extend(more)\n return more\n\n def load_full(self, extra_payload=()):\n self.load_more(self.queue.maxlen - len(self.queue), extra_payload)\n\n def load_update(self, unread_count, extra_payload=()):\n assert isinstance(unread_count, int) and unread_count >= 0\n if unread_count == 0:\n return []\n\n if len(self) == 0:\n current_latest_id = None\n elif self[0].id is not None:\n current_latest_id = self[0].id\n else:\n current_latest_id = self[1].id\n\n payload = {\n 'trigger': 'user',\n 'limit': unread_count,\n 'loadMoreKey': None\n }\n payload.update(self.fixed_extra_payload)\n payload.update(dict(extra_payload))\n result = super().fetch_more(self.endpoint, payload)\n updates = []\n for item in [t for t in result['data'] if t.get('type') != 'PERSONAL_UPDATE_SECTION']:\n if item['id'] != current_latest_id:\n updates.append(converter[item['type']](**item))\n else:\n break\n self.extendleft(reversed(updates))\n return updates\n\n\nclass JikeEmitter(JikeFetcher):\n def __init__(self, jike_session, endpoint, fixed_extra_payload=()):\n super().__init__(jike_session)\n self.stopped = False\n self.endpoint = endpoint\n self.fixed_extra_payload = dict(fixed_extra_payload)\n\n def __repr__(self):\n return 'JikeEmitter({})'.format(repr(self.jike_session))\n\n def generate(self):\n while not self.stopped:\n payload = {\n 'trigger': 'user',\n 'limit': 100,\n 'loadMoreKey': self.load_more_key,\n }\n payload.update(self.fixed_extra_payload)\n result = super().fetch_more(self.endpoint, payload)\n try:\n self.load_more_key = result['loadMoreKey']\n except KeyError:\n self.load_more_key = None\n if self.load_more_key is None:\n self.stopped = True\n\n for item in result['data']:\n yield item\n\n def stop(self):\n self.stopped = True\n" }, { "path": "jike/objects/message.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\ncontaining objects:\n\n- OfficialMessage\n- OriginalPost\n- Repost\n- Question\n\"\"\"\n\nfrom collections import namedtuple\nfrom .wrapper import namedtuple_with_defaults\n\nPUBLIC_FIELDS = [\n # item meta info\n 'id',\n 'createdAt',\n 'content',\n 'pictures',\n 'video',\n 'audio',\n 'status',\n 'topic',\n 'linkInfo',\n 'target',\n 'targetType',\n 'type',\n 'user',\n 'isCommentForbidden',\n 'viewType',\n 'urlsInText',\n 'isFeatured',\n # item interaction info\n 'likeCount',\n 'likeIcon',\n 'likeInfo',\n 'commentCount',\n 'repostCount',\n # item personal info\n 'read',\n 'liked',\n 'collected',\n 'collectedTime',\n 'collectTime', # seems to be Jike typo\n 'rollouts',\n]\n\n# Object type for type: 'OFFICIAL_MESSAGE'\nOfficialMessage = namedtuple_with_defaults(\n namedtuple('OfficialMessage',\n list(set(PUBLIC_FIELDS + [\n 'abstract',\n 'video',\n ])))\n)\n\n# Object type for type: 'ORIGINAL_POST'\nOriginalPost = namedtuple_with_defaults(\n namedtuple('OriginalPost',\n list(set(PUBLIC_FIELDS + [\n 'messageId',\n 'poi',\n ])))\n)\n\n# Object type for type: 'REPOST'\nRepost = namedtuple_with_defaults(\n namedtuple(\"Repost\",\n list(set(PUBLIC_FIELDS + [\n 'syncCommentId',\n 'replyToComment',\n ])))\n)\n\n# Object type for type: 'QUESTION'\nQuestion = namedtuple_with_defaults(\n namedtuple('Question',\n list(set(PUBLIC_FIELDS + [\n 'answerCount',\n 'title',\n 'updatedAt',\n 'userAnswerId',\n ])))\n)\n\n# Object type for type: 'ANSWER'\nAnswer = namedtuple_with_defaults(\n namedtuple('Answer',\n list(set(PUBLIC_FIELDS + [\n 'question',\n 'questionId',\n 'richtextContent',\n 'upVoteCount',\n 'voteTend',\n ])))\n)\n\n# Object type for type: 'PERSONAL_UPDATE_SECTION'\nPersonalUpdateSection = namedtuple_with_defaults(\n namedtuple('PersonalUpdateSection',\n list(set(PUBLIC_FIELDS + [\n 'items',\n ])))\n)\n\n# Object type for type: 'PERSONAL_UPDATE'\nPersonalUpdate = namedtuple_with_defaults(\n namedtuple('PersonalUpdate',\n list(set(PUBLIC_FIELDS + [\n 'action',\n 'actionTime',\n 'topicIds',\n 'topics',\n 'targetUsernames',\n 'targetUsers',\n 'updateIds',\n 'usernames',\n 'users',\n ])))\n)\n\n# Object type for type: 'COMMENT'\nComment = namedtuple_with_defaults(\n namedtuple('Comment',\n list(set(PUBLIC_FIELDS + [\n 'enablePictureComments',\n 'hotReplies',\n 'level',\n 'replyCount',\n 'targetId',\n 'targetType',\n 'threadId',\n ])))\n)\n" }, { "path": "jike/objects/topic.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nObject type for topic\n\n\"\"\"\n\nfrom collections import namedtuple\nfrom .wrapper import namedtuple_with_defaults\n\nTopic = namedtuple_with_defaults(\n namedtuple('Topic',\n [\n 'briefIntro',\n 'content',\n 'createdAt',\n 'enableForUserPost',\n 'enablePictureComments',\n 'friendsAlsoSubscribe',\n 'id',\n 'isAnonymous',\n 'isDreamTopic',\n 'isValid',\n 'keywords',\n 'lastMessagePostTime',\n 'likeIcon',\n 'maintainer',\n 'messagePrefix',\n 'newCategory',\n 'operateStatus',\n 'pictureUrl',\n 'rectanglePicture',\n 'ref',\n 'squarePicture',\n 'subscribedAt',\n 'subscribedStatusRawValue',\n 'subscribersCount',\n 'thumbnailUrl',\n 'timeForRank',\n 'topicId',\n 'topicType',\n 'type',\n 'updatedAt'\n ])\n)\n" }, { "path": "jike/objects/user.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nObject type for user\n\n\"\"\"\n\nfrom collections import namedtuple\nfrom .wrapper import namedtuple_with_defaults\nfrom ..constants import ENDPOINTS\n\nUser = namedtuple_with_defaults(\n namedtuple('User',\n [\n 'areaCode',\n 'avatarImage',\n 'backgroundImage',\n 'bio',\n 'birthday',\n 'briefIntro',\n 'city',\n 'country',\n 'createdAt',\n 'following',\n 'gender',\n 'id',\n 'initUsername',\n 'isBanned',\n 'isBetaUser',\n 'isFriendlyUser',\n 'isLoginUser',\n 'isVerified',\n 'medals',\n 'mobilePhoneNumber',\n 'preferences',\n 'profileTags',\n 'province',\n 'profileImageUrl',\n 'ref',\n 'qqOpenId',\n 'qqUserInfo',\n 'school',\n 'screenName',\n 'updatedAt',\n 'userId',\n 'username',\n 'usernameSet',\n 'verifyMessage',\n 'wechatOpenId',\n 'wechatUserInfo',\n 'weiboUid',\n 'weiboUserInfo',\n 'zodiac',\n\n 'followedCount',\n 'followingCount',\n 'highlightedPersonalUpdates',\n 'liked',\n 'topicCreated',\n 'topicSubscribed',\n\n 'groupId',\n 'groupVersion',\n 'industry',\n 'lastChangeNameTime',\n 'statsCount',\n 'trailingIcons',\n 'wechatUnionId',\n ])\n)\nUser.__repr__ = lambda user: 'User(screenName={screenName})'.format(\n screenName=user.screenName)\n" }, { "path": "jike/objects/wrapper.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nwrapper\n\"\"\"\n\n\ndef repr_namedtuple(nt):\n return nt.__class__.__name__ + '(id={id}, content={content})'.format(id=nt.id, content=nt.content)\n\n\ndef str_namedtuple(nt):\n return nt.__class__.__name__ + '(' + ', '.join(\n '{}={}'.format(k, v) for k, v in zip(nt._fields, nt) if v is not None) + ')'\n\n\ndef namedtuple_with_defaults(namedtuple):\n namedtuple.__new__.__defaults__ = (None,) * len(namedtuple._fields)\n namedtuple.__repr__ = repr_namedtuple\n namedtuple.__str__ = str_namedtuple\n return namedtuple\n" }, { "path": "jike/qr_code.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nQR code that be scanned to allow login\n\"\"\"\n\nimport qrcode\nimport tempfile\nimport webbrowser\nfrom decimal import Decimal\nfrom qrcode.image.svg import SvgImage\nfrom .constants import JIKE_URI_SCHEME_FMT, RENDER2BROWSER_HTML_TEMPLATE\n\n\ndef make_qrcode(uuid, render_choice='browser'):\n qr = qrcode.QRCode(\n version=8,\n error_correction=qrcode.constants.ERROR_CORRECT_L,\n box_size=4,\n border=4,\n )\n qr.add_data(JIKE_URI_SCHEME_FMT.format(**uuid))\n qr.make()\n\n render_choices = {\n 'browser': render2browser,\n 'terminal': render2terminal,\n 'viewer': render2viewer\n }\n\n assert render_choice in render_choices, 'Unsupported render choice.\\nAvailable choices: browser, viewer, terminal'\n render_choices[render_choice](qr)\n\n\ndef render2terminal(qr):\n qr.print_tty()\n\n\ndef render2browser(qr):\n img = qr.make_image(image_factory=JikeSvgPathImage)\n with tempfile.NamedTemporaryFile(suffix='.svg') as fp:\n img.save(fp)\n fp.seek(0)\n content = fp.read().decode('utf-8').splitlines()\n svg = content[1]\n assert svg.startswith(''), 'Render QR code fail'\n html = RENDER2BROWSER_HTML_TEMPLATE.substitute(qrcode_svg=svg)\n\n _, path = tempfile.mkstemp(suffix='.html')\n with open(path, 'wt', encoding='utf-8') as fp:\n fp.write(html)\n webbrowser.open('file://{}'.format(fp.name))\n\n\ndef render2viewer(qr):\n img = qr.make_image()\n _, path = tempfile.mkstemp(suffix='.png')\n with open(path, 'wb') as fp:\n img.save(fp)\n webbrowser.open('file://{}'.format(fp.name))\n\n\nclass JikeSvgPathImage(SvgImage):\n def units(self, pixels, text=True):\n \"\"\"\n A box_size of 10 (default) equals 3.3mm.\n \"\"\"\n units = Decimal(pixels) / 3\n if not text:\n return units\n return '{:.2f}mm'.format(units)\n" }, { "path": "jike/session.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nSession that communicates with Jike server\n\"\"\"\n\nimport requests\nfrom .constants import HEADERS\n\n\nclass JikeSession:\n def __init__(self, token):\n self.session = requests.Session()\n self.token = token\n self.headers = dict(HEADERS)\n self.headers.update({'x-jike-app-auth-jwt': token})\n\n def __del__(self):\n self.session.close()\n\n def __repr__(self):\n return 'JikeSession({}...{})'.format(self.token[:10], self.token[-10:])\n\n def get(self, url, params=None):\n return self.session.get(url, params=params, headers=self.headers)\n\n def post(self, url, params=None, json=None):\n return self.session.post(url, params=params, json=json, headers=self.headers)\n" }, { "path": "jike/utils.py", "content": "# -*- coding: utf-8 -*-\n\n\"\"\"\nutils\n\"\"\"\n\nimport requests\nimport json\nimport os\nimport platform\nfrom collections import defaultdict\nfrom mimetypes import guess_type\n\nfrom .qr_code import make_qrcode\nfrom .constants import ENDPOINTS, AUTH_TOKEN_STORE_PATH, URL_VALIDATION_PATTERN\nfrom .objects.message import *\n\nconverter = defaultdict(lambda: dict,\n {\n 'OFFICIAL_MESSAGE': OfficialMessage,\n 'ORIGINAL_POST': OriginalPost,\n 'QUESTION': Question,\n 'ANSWER': Answer,\n 'REPOST': Repost,\n 'PERSONAL_UPDATE': PersonalUpdate,\n 'PERSONAL_UPDATE_SECTION': PersonalUpdateSection,\n 'COMMENT': Comment,\n })\n\n\ndef read_token():\n if os.path.exists(AUTH_TOKEN_STORE_PATH):\n with open(AUTH_TOKEN_STORE_PATH, 'rt', encoding='utf-8') as fp:\n store = json.load(fp)\n return store['auth_token']\n\n\ndef write_token(token):\n with open(AUTH_TOKEN_STORE_PATH, 'wt', encoding='utf-8') as fp:\n store = {\n 'auth_token': token\n }\n json.dump(store, fp, indent=2)\n\n\ndef wait_login(uuid):\n res = requests.get(ENDPOINTS['wait_login'], params=uuid)\n if res.status_code == 200:\n logged_in = res.json()\n return logged_in['logged_in']\n res.raise_for_status()\n return False\n\n\ndef confirm_login(uuid):\n res = requests.get(ENDPOINTS['confirm_login'], params=uuid)\n if res.status_code == 200:\n confirmed = res.json()\n if confirmed['confirmed'] is True:\n return confirmed['token']\n else:\n raise SystemExit('User not board Jike Metro, what a shame')\n res.raise_for_status()\n\n\ndef login():\n res = requests.get(ENDPOINTS['create_session'])\n uuid = None\n if res.status_code == 200:\n uuid = res.json()\n res.raise_for_status()\n\n assert uuid, 'Create session fail'\n make_qrcode(uuid)\n\n logging = False\n attempt_counter = 1\n while not logging:\n logging = wait_login(uuid)\n attempt_counter += 1\n if attempt_counter > 5:\n raise SystemExit('Login takes too long, abort')\n\n token = None\n attempt_counter = 1\n while token is None:\n token = confirm_login(uuid)\n attempt_counter += 1\n if attempt_counter > 5:\n raise SystemExit('Login takes too long, abort')\n\n return token\n\n\ndef extract_url(content):\n return URL_VALIDATION_PATTERN.findall(content)\n\n\ndef extract_link(jike_session, link):\n res = jike_session.post(ENDPOINTS['extract_link'], json={\n 'link': link\n })\n link_info = None\n if res.status_code == 200:\n result = res.json()\n if result['success']:\n link_info = result['data']\n res.raise_for_status()\n return link_info\n\n\ndef get_uptoken():\n res = requests.get(ENDPOINTS['picture_uptoken'], params={'bucket': 'jike'})\n if res.ok:\n return res.json()['uptoken']\n res.raise_for_status()\n\n\ndef upload_a_picture(picture):\n assert os.path.exists(picture)\n name = os.path.split(picture)[1]\n mimetype, _ = guess_type(name)\n assert mimetype\n if not mimetype.startswith('image'):\n raise ValueError('Cannot upload file: {}, which is not picture'.format(name))\n\n uptoken = get_uptoken()\n with open(picture, 'rb') as fp:\n files = {'token': (None, uptoken), 'file': (name, fp, mimetype)}\n res = requests.post(ENDPOINTS['picture_upload'], files=files)\n if res.status_code == 200:\n result = res.json()\n if result['success']:\n return result['key']\n else:\n raise RuntimeError('Picture upload fail')\n res.raise_for_status()\n\n\ndef upload_pictures(picture_paths):\n if isinstance(picture_paths, str):\n picture_paths = [picture_paths]\n pic_url = [upload_a_picture(picture) for picture in picture_paths]\n return pic_url\n\n\ndef notify(title, message):\n assert isinstance(title, str), 'please provide string as title'\n assert isinstance(message, str), 'please provide string as message'\n if 'Darwin' not in platform.system():\n return 'Only support macOS system'\n cmd = \"\"\"/usr/bin/osascript -e 'display notification \"{msg}\" with title \"{title}\"'\"\"\".format(title=title, msg=message)\n os.system(cmd)\n" }, { "path": "nlp/generate_dataset.py", "content": "# -*- coding: utf-8 -*-\n\nfrom faker import Faker\nimport random\nfrom babel.dates import format_time\n\nfake = Faker()\nfake.seed(1234)\nrandom.seed(1234)\n\nTRANS_TABLE = str.maketrans({\n '1': '一',\n '2': '二',\n '3': '三',\n '4': '四',\n '5': '五',\n '6': '六',\n '7': '七',\n '8': '八',\n '9': '九',\n '0': '零'\n})\n\nABS_FORMATS = [\n 'short',\n 'medium',\n 'ah点m分',\n 'ah点m分s秒',\n 'ah点mm分',\n 'ah点mm分ss秒',\n 'H点m分',\n 'H点m分s秒',\n 'H点mm分',\n 'H点mm分ss秒',\n]\n\nREL_PREFIXS = {\n '今天': '+0D',\n '明天': '+1D',\n '后天': '+2D',\n '大后天': '+3D',\n '下周': '+7D',\n\n '周日': 'TW7',\n '这周日': 'TW7',\n '本周日': 'TW7',\n '下周日': 'NW7',\n\n '?秒后': '+?S',\n '?秒以后': '+?S',\n '再过?秒': '+?S',\n '?分钟后': '+?M',\n '?分钟以后': '+?M',\n '再过?分钟': '+?M',\n '?小时后': '+?H',\n '?小时以后': '+?H',\n '再过?小时': '+?H',\n}\n\nfor i in range(1, 8):\n REL_PREFIXS.update({\n '周{}'.format(str(i).translate(TRANS_TABLE)): 'TW{}'.format(i),\n '这周{}'.format(str(i).translate(TRANS_TABLE)): 'TW{}'.format(i),\n '本周{}'.format(str(i).translate(TRANS_TABLE)): 'TW{}'.format(i),\n '下周{}'.format(str(i).translate(TRANS_TABLE)): 'NW{}'.format(i),\n })\n\n\ndef translate_two_digits(digits):\n digits = str(digits)\n assert 1 <= len(digits) <= 2\n if len(digits) == 1:\n return digits.translate(TRANS_TABLE)\n else:\n tens = digits[0].translate(TRANS_TABLE) if 2 <= int(digits[0]) <= 9 else ''\n units = digits[1].translate(TRANS_TABLE) if int(digits[1]) != 0 else ''\n return '{}十{}'.format(tens, units)\n\n\ndef generate_date():\n dt = fake.future_datetime(end_date='+30d')\n\n machine_readable = dt.strftime('%H:%M:%S')\n human_readable = format_time(dt, format=random.choice(ABS_FORMATS), locale='zh_CN')\n\n r = random.random()\n if r < 0.3:\n machine_readable = 'ABS>' + machine_readable\n elif 0.3 <= r <= 0.7:\n prefix = random.choice([k for k in REL_PREFIXS.keys() if '?' not in k])\n machine_readable = REL_PREFIXS[prefix] + '>' + machine_readable\n human_readable = prefix + human_readable\n else:\n prefix = random.choice([k for k in REL_PREFIXS.keys() if '?' in k])\n machine_readable = REL_PREFIXS[prefix]\n human_readable = prefix\n\n return human_readable, machine_readable, dt\n\n\ndef generate_dataset(m):\n lines = []\n for i in range(m):\n h, m, _ = generate_date()\n lines.append(h + ',' + m + '\\n')\n\n with open('dataset.txt', 'wt', encoding='utf-8') as f:\n f.writelines(lines)\n\n\nif __name__ == '__main__':\n generate_dataset(int(1e3))\n" }, { "path": "setup.cfg", "content": "[metadata]\nlicense_file = LICENSE\n" }, { "path": "setup.py", "content": "import sys\nimport os\nfrom setuptools import setup, find_packages\nfrom codecs import open\n\nhere = os.path.abspath(os.path.dirname(__file__))\n\n# 'setup.py publish' shortcut.\nif sys.argv[-1] == 'publish':\n os.system('python setup.py bdist_wheel')\n os.system('twine upload dist/*')\n sys.exit()\n\nwith open(os.path.join(here, 'README.rst'), encoding='utf-8') as f:\n readme = f.read()\n\nabout = {}\nwith open(os.path.join(here, 'jike', '__init__.py'), encoding='utf-8') as f:\n exec('\\n'.join(filter(lambda l: l.startswith('__'), f.readlines())), about)\n\nsetup(\n name=about['__title__'],\n version=about['__version__'],\n description=about['__description__'],\n long_description=readme,\n url=about['__url__'],\n author=about['__author__'],\n author_email=about['__author_email__'],\n license=about['__license__'],\n python_requires=\"!=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*\",\n classifiers=[\n # 4 - Beta\n # 5 - Production/Stable\n 'Development Status :: 4 - Beta',\n 'Intended Audience :: Developers',\n 'Topic :: Software Development :: Build Tools',\n 'Topic :: Internet :: WWW/HTTP :: Browsers',\n 'License :: OSI Approved :: MIT License',\n 'Programming Language :: Python :: 3',\n 'Programming Language :: Python :: 3.4',\n 'Programming Language :: Python :: 3.5',\n 'Programming Language :: Python :: 3.6',\n 'Programming Language :: Python :: Implementation :: CPython',\n 'Programming Language :: Python :: Implementation :: PyPy'\n ],\n keywords=about['__keywords__'],\n packages=find_packages(exclude=['docs', 'tests']),\n package_dir={\n 'jike': 'jike'\n },\n install_requires=[\n 'requests>=2.18.0',\n 'pillow>=3.4.0',\n 'qrcode>=5.3',\n ],\n extras_require={\n 'test': ['responses>=0.8.0'],\n 'doc': ['nbconvert>=5.3.0']\n },\n tests_require=['responses>=0.8.0'],\n project_urls={\n 'Bug Reports': 'https://github.com/Sorosliu1029/Jike-Metro/issues',\n 'Say Thanks!': 'http://saythanks.io/to/Sorosliu1029',\n 'Source': 'https://github.com/Sorosliu1029/Jike-Metro/',\n },\n)\n" }, { "path": "tests/__init__.py", "content": "" }, { "path": "tests/test_base.py", "content": "import unittest\nimport requests\nfrom unittest.mock import *\nfrom jike.objects.base import *\n\n\nclass TestJikeSequenceBase(unittest.TestCase):\n def setUp(self):\n self.mock_a = Mock()\n self.mock_a.id = 1\n self.mock_b = Mock()\n self.mock_b.id = 2\n\n self.sequence = JikeSequenceBase()\n\n def test_init(self):\n self.assertEqual(self.sequence.seq, [])\n\n def test_repr(self):\n self.assertEqual(repr(self.sequence), 'JikeSequenceBase(0 items)')\n\n def test_len(self):\n self.assertEqual(len(self.sequence), 0)\n\n def test_append(self):\n self.sequence.append(self.mock_a)\n self.assertEqual(len(self.sequence), 1)\n self.assertEqual(self.sequence[0], self.mock_a)\n\n def test_getitem(self):\n with self.assertRaises(IndexError):\n _ = self.sequence[0]\n self.sequence.append(self.mock_a)\n self.assertEqual(self.sequence[0], self.mock_a)\n\n def test_contains(self):\n self.assertFalse(self.mock_a in self.sequence)\n self.sequence.append(self.mock_a)\n self.assertTrue(self.mock_a in self.sequence)\n\n def test_extend(self):\n self.sequence.extend([self.mock_a, self.mock_b])\n self.assertEqual(len(self.sequence), 2)\n self.assertEqual(self.sequence[0], self.mock_a)\n self.assertEqual(self.sequence[-1], self.mock_b)\n\n def test_reversed(self):\n self.sequence.extend([self.mock_a, self.mock_b])\n self.assertEqual(list(reversed(self.sequence))[0], self.mock_b)\n self.assertEqual(list(reversed(self.sequence))[-1], self.mock_a)\n\n def test_index(self):\n self.sequence.append(self.mock_a)\n self.assertEqual(self.sequence.index(self.mock_a), 0)\n with self.assertRaises(ValueError):\n self.sequence.index(self.mock_b)\n\n def test_clear(self):\n self.sequence.append(self.mock_a)\n self.assertEqual(len(self.sequence), 1)\n self.sequence.clear()\n self.assertEqual(len(self.sequence), 0)\n\n\nclass TestJikeStreamBase(unittest.TestCase):\n def setUp(self):\n self.mock_a = Mock()\n self.mock_a.id = 1\n self.mock_b = Mock()\n self.mock_b.id = 2\n self.mock_c = Mock()\n self.mock_c.id = 3\n\n self.stream = JikeStreamBase(maxlen=2)\n\n def test_init(self):\n self.assertEqual(len(self.stream.queue), 0)\n self.assertEqual(self.stream.queue.maxlen, 2)\n\n def test_repr(self):\n self.assertEqual(repr(self.stream), 'JikeStreamBase(0 items)')\n\n def test_getitem(self):\n with self.assertRaises(IndexError):\n _ = self.stream[0]\n self.stream.append(self.mock_a)\n self.assertEqual(self.stream[0], self.mock_a)\n\n def test_contains(self):\n self.assertFalse(self.mock_a in self.stream)\n self.stream.append(self.mock_a)\n self.assertTrue(self.mock_a in self.stream)\n\n def test_len(self):\n self.assertEqual(len(self.stream), 0)\n\n def test_reversed(self):\n self.stream.extend([self.mock_a, self.mock_b])\n self.assertEqual(list(reversed(self.stream))[0], self.mock_b)\n self.assertEqual(list(reversed(self.stream))[-1], self.mock_a)\n\n def test_index(self):\n self.stream.append(self.mock_a)\n self.assertEqual(self.stream.index(self.mock_a), 0)\n with self.assertRaises(ValueError):\n self.stream.index(self.mock_b)\n\n def test_append(self):\n self.stream.append(self.mock_a)\n self.assertEqual(len(self.stream), 1)\n self.assertEqual(self.stream[0], self.mock_a)\n\n def test_appendleft(self):\n self.stream.append(self.mock_a)\n self.stream.appendleft(self.mock_b)\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[1], self.mock_a)\n self.assertEqual(self.stream[0], self.mock_b)\n\n def test_clear(self):\n self.stream.append(self.mock_a)\n self.assertEqual(len(self.stream), 1)\n self.stream.clear()\n self.assertEqual(len(self.stream), 0)\n\n def test_extend(self):\n self.stream.extend([self.mock_a, self.mock_b])\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0], self.mock_a)\n self.assertEqual(self.stream[-1], self.mock_b)\n\n def test_extendleft(self):\n self.stream.extendleft([self.mock_a, self.mock_b])\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0], self.mock_b)\n self.assertEqual(self.stream[-1], self.mock_a)\n\n def test_pop(self):\n self.stream.append(self.mock_a)\n popped = self.stream.pop()\n self.assertEqual(len(self.stream), 0)\n self.assertEqual(popped, self.mock_a)\n with self.assertRaises(IndexError):\n self.stream.pop()\n\n def test_popleft(self):\n self.stream.append(self.mock_a)\n self.stream.appendleft(self.mock_b)\n popped = self.stream.popleft()\n self.assertEqual(len(self.stream), 1)\n self.assertEqual(popped, self.mock_b)\n _ = self.stream.popleft()\n self.assertEqual(len(self.stream), 0)\n with self.assertRaises(IndexError):\n self.stream.popleft()\n\n\nclass TestJikeFetcher(unittest.TestCase):\n def setUp(self):\n self.mock_session = Mock()\n self.fetcher = JikeFetcher(self.mock_session)\n\n def test_init(self):\n self.assertEqual(self.fetcher.jike_session, self.mock_session)\n self.assertIsNone(self.fetcher.load_more_key)\n\n def test_repr(self):\n self.assertEqual(repr(self.fetcher), 'JikeFetcher({})'.format(repr(self.mock_session)))\n\n def test_fetch_more(self):\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'more': 'items'}\n mock_response.raise_for_status.return_value = None\n self.mock_session.post.return_value = mock_response\n self.assertEqual(self.fetcher.fetch_more(None, None), {'more': 'items'})\n mock_response.status_code = 404\n mock_response.raise_for_status.side_effect = requests.HTTPError\n with self.assertRaises(requests.HTTPError):\n self.fetcher.fetch_more(None, None)\n\n\nclass TestList(unittest.TestCase):\n def setUp(self):\n self.mock_session = Mock()\n self.list = List(self.mock_session, 'https://ojbk.com/', type_converter=dict)\n\n def test_init(self):\n self.assertEqual(self.list.endpoint, 'https://ojbk.com/')\n self.assertEqual(self.list.fixed_extra_payload, {})\n self.assertIs(self.list.converter, dict)\n\n def test_repr(self):\n self.assertEqual(repr(self.list), 'List(0 items)')\n\n def test_load_more(self):\n data = [{'id': 'b'}, {'id': 'c'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'loadMoreKey': 'a', 'data': data}\n self.mock_session.post.return_value = mock_response\n result = self.list.load_more()\n self.assertEqual(result, data)\n self.assertEqual(self.list.load_more_key, 'a')\n self.assertEqual(len(self.list), 2)\n self.assertEqual(self.list[0], {'id': 'b'})\n self.mock_session.post.assert_called_once_with('https://ojbk.com/', json={\n 'limit': 20,\n 'loadMoreKey': None\n })\n mock_response.json.return_value = {'loadMoreKey': 'd', 'data': []}\n result = self.list.load_more(limit=10)\n self.assertEqual(result, [])\n self.assertEqual(self.list.load_more_key, 'd')\n self.assertEqual(len(self.list), 2)\n self.mock_session.post.assert_called_with('https://ojbk.com/', json={\n 'limit': 10,\n 'loadMoreKey': 'a'\n })\n\n def test_load_all(self):\n data = [{'id': 'b'}, {'id': 'c'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.side_effect = [\n {'loadMoreKey': 'a', 'data': data},\n {'loadMoreKey': None, 'data': data}\n ]\n self.mock_session.post.return_value = mock_response\n result = self.list.load_all()\n self.assertEqual(result, 4)\n self.assertIsNone(self.list.load_more_key)\n self.assertEqual(self.mock_session.post.call_count, 2)\n\n\nclass TestStream(unittest.TestCase):\n def setUp(self):\n self.mock_session = Mock()\n self.stream = Stream(self.mock_session, 'https://ojbk.com/', maxlen=2)\n\n def test_init(self):\n self.assertEqual(self.stream.endpoint, 'https://ojbk.com/')\n self.assertEqual(self.stream.fixed_extra_payload, {})\n self.assertEqual(self.stream.queue.maxlen, 2)\n\n def test_repr(self):\n self.assertEqual(repr(self.stream), 'Stream(0 items, with 2 capacity)')\n\n def test_load_more(self):\n data1 = [{'id': 'b', 'type': 'OFFICIAL_MESSAGE'}, {'id': 'c', 'type': 'OFFICIAL_MESSAGE'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'loadMoreKey': 'a', 'data': data1}\n self.mock_session.post.return_value = mock_response\n result = self.stream.load_more()\n self.assertIsInstance(result[0], tuple)\n self.assertEqual(self.stream.load_more_key, 'a')\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0].id, 'b')\n self.assertEqual(self.stream[-1].type, 'OFFICIAL_MESSAGE')\n self.mock_session.post.assert_called_once_with('https://ojbk.com/', json={\n 'trigger': 'user',\n 'limit': 20,\n 'loadMoreKey': None\n })\n data2 = [{'id': 'd', 'type': 'REPOST'}, {'id': 'e', 'type': 'REPOST'}]\n mock_response.json.return_value = {'loadMoreKey': None, 'data': data2}\n result = self.stream.load_more(limit=10)\n self.assertIsInstance(result[0], tuple)\n self.assertIsNone(self.stream.load_more_key)\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0].id, 'd')\n self.assertEqual(self.stream[-1].type, 'REPOST')\n self.mock_session.post.assert_called_with('https://ojbk.com/', json={\n 'trigger': 'user',\n 'limit': 10,\n 'loadMoreKey': 'a'\n })\n\n def test_load_full(self):\n data1 = [{'id': 'b', 'type': 'OFFICIAL_MESSAGE'}, {'id': 'c', 'type': 'OFFICIAL_MESSAGE'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'loadMoreKey': 'a', 'data': data1}\n self.mock_session.post.return_value = mock_response\n self.stream.append(None)\n self.stream.load_full()\n self.assertEqual(self.stream.load_more_key, 'a')\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0].id, 'b')\n self.assertEqual(self.stream[-1].type, 'OFFICIAL_MESSAGE')\n self.mock_session.post.assert_called_once_with('https://ojbk.com/', json={\n 'trigger': 'user',\n 'limit': 1,\n 'loadMoreKey': None\n })\n\n def test_load_update(self):\n data1 = [{'id': 'b', 'type': 'OFFICIAL_MESSAGE'}, {'id': 'c', 'type': 'OFFICIAL_MESSAGE'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'loadMoreKey': 'a', 'data': data1}\n self.mock_session.post.return_value = mock_response\n result = self.stream.load_update(1)\n self.assertIsInstance(result[0], tuple)\n self.assertEqual(len(self.stream), 2)\n self.assertEqual(self.stream[0].id, 'b')\n self.assertEqual(self.stream[-1].type, 'OFFICIAL_MESSAGE')\n self.mock_session.post.assert_called_once_with('https://ojbk.com/', json={\n 'trigger': 'user',\n 'limit': 1,\n 'loadMoreKey': None\n })\n result = self.stream.load_update(0)\n self.assertEqual(result, [])\n with self.assertRaises(AssertionError):\n self.stream.load_update('not an int')\n with self.assertRaises(AssertionError):\n self.stream.load_update(-1)\n\n\nclass TestJikeEmitter(unittest.TestCase):\n def setUp(self):\n self.mock_session = Mock()\n self.emitter = JikeEmitter(self.mock_session, 'https://ojbk.com/')\n\n def test_init(self):\n self.assertFalse(self.emitter.stopped)\n self.assertEqual(self.emitter.endpoint, 'https://ojbk.com/')\n self.assertEqual(self.emitter.fixed_extra_payload, {})\n\n def test_repr(self):\n self.assertEqual(repr(self.emitter), 'JikeEmitter({})'.format(repr(self.mock_session)))\n\n def test_generate(self):\n data1 = [{'id': 'b'}, {'id': 'c'}]\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.side_effect = [\n {'loadMoreKey': 'a', 'data': data1},\n {'loadMoreKey': None, 'data': []}\n ]\n self.mock_session.post.return_value = mock_response\n result = list(self.emitter.generate())\n self.assertEqual(result, data1)\n self.assertTrue(self.emitter.stopped)\n self.assertIsNone(self.emitter.load_more_key)\n self.mock_session.post.assert_called_with('https://ojbk.com/', json={\n 'trigger': 'user',\n 'limit': 100,\n 'loadMoreKey': 'a'\n })\n self.assertEqual(self.mock_session.post.call_count, 2)\n\n def test_stop(self):\n self.assertFalse(self.emitter.stopped)\n self.emitter.stop()\n self.assertTrue(self.emitter.stopped)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_client.py", "content": "import unittest\nimport requests\nfrom unittest.mock import *\n\nfrom jike.client import JikeClient\n\n\nclass TestJikeClient(unittest.TestCase):\n def setUp(self):\n self.read_token = patch('jike.client.read_token').start()\n self.timer_start = patch('jike.client.Timer.start').start()\n\n self.MockJikeSession = patch('jike.client.JikeSession').start()\n self.mock_jike_session = Mock()\n self.MockJikeSession.return_value = self.mock_jike_session\n\n self.MockList = patch('jike.client.List').start()\n self.MockStream = patch('jike.client.Stream').start()\n\n self.MockUser = patch('jike.client.User').start()\n self.mock_user = Mock()\n self.MockUser.return_value = self.mock_user\n\n self.read_token.return_value = 'token'\n self.timer_start.return_value = None\n self.jike_client = JikeClient(sync_unread=True)\n\n def tearDown(self):\n del self.jike_client.jike_session\n patch.stopall()\n\n def test_init(self):\n self.assertEqual(self.jike_client.auth_token, 'token')\n self.assertIsNotNone(self.jike_client.jike_session)\n self.assertIsNone(self.jike_client.collection)\n self.assertIsNone(self.jike_client.news_feed)\n self.assertIsNone(self.jike_client.following_update)\n self.assertEqual(self.jike_client.unread_count, 0)\n self.read_token.assert_called_once()\n self.MockJikeSession.assert_called_once()\n self.timer_start.assert_called_once()\n # first login\n self.read_token.return_value = None\n with patch('jike.client.login', return_value='login_token') as login, \\\n patch('jike.client.write_token', return_value=None) as token_write:\n JikeClient()\n login.assert_called_once()\n token_write.assert_called_once()\n\n @patch.object(JikeClient, 'get_user_profile')\n def test_get_my_profile(self, mock_get_user_profile):\n profile = {'user': 'jike'}\n mock_get_user_profile.return_value = profile\n result = self.jike_client.get_my_profile()\n self.assertEqual(result, profile)\n mock_get_user_profile.assert_called_once_with(username=None)\n\n def test_get_my_collection(self):\n mock_collection = Mock()\n self.MockList.return_value = mock_collection\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_my_collection()\n self.assertEqual(result, mock_collection)\n self.assertEqual(self.jike_client.collection, mock_collection)\n self.MockList.assert_called_once()\n mock_collection.load_more.assert_called_once()\n # second call\n self.MockList.reset_mock()\n result = self.jike_client.get_my_collection()\n self.assertEqual(result, mock_collection)\n self.MockList.assert_not_called()\n\n def test_get_news_feed_unread_count(self):\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'newMessageCount': 0}\n self.mock_jike_session.get.return_value = mock_response\n result = self.jike_client.get_news_feed_unread_count()\n self.assertEqual(result, 0)\n self.assertEqual(self.jike_client.unread_count, 0)\n # failed call\n mock_response.status_code = 404\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.get_news_feed_unread_count()\n\n def test_get_news_feed(self):\n mock_news_feed = Mock()\n self.MockStream.return_value = mock_news_feed\n self.MockStream.load_more.return_value = None\n result = self.jike_client.get_news_feed()\n self.assertEqual(result, mock_news_feed)\n self.assertEqual(self.jike_client.news_feed, mock_news_feed)\n self.MockStream.assert_called_once()\n mock_news_feed.load_more.assert_called_once()\n # second call\n self.MockStream.reset_mock()\n result = self.jike_client.get_news_feed()\n self.assertEqual(result, mock_news_feed)\n self.MockStream.assert_not_called()\n\n def test_get_following_update(self):\n mock_following_update = Mock()\n self.MockStream.return_value = mock_following_update\n self.MockStream.load_more.return_value = None\n result = self.jike_client.get_following_update()\n self.assertEqual(result, mock_following_update)\n self.assertEqual(self.jike_client.following_update, mock_following_update)\n self.MockStream.assert_called_once()\n mock_following_update.load_more.assert_called_once()\n # second call\n self.MockStream.reset_mock()\n result = self.jike_client.get_following_update()\n self.assertEqual(result, mock_following_update)\n self.MockStream.assert_not_called()\n\n def test_get_user_profile(self):\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'user': {'name': 'jike'}, 'statsCount': {'count': 1}}\n self.mock_jike_session.get.return_value = mock_response\n result = self.jike_client.get_user_profile('jike')\n self.assertEqual(result, self.mock_user)\n # failed call\n mock_response.status_code = 401\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.get_user_profile('jike')\n\n def test_get_user_post(self):\n mock_posts = Mock()\n self.MockList.return_value = mock_posts\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_user_post('jike')\n self.assertEqual(result, mock_posts)\n self.MockList.assert_called_once()\n mock_posts.load_more.assert_called_once()\n\n def test_get_user_created_topic(self):\n mock_topics = Mock()\n self.MockList.return_value = mock_topics\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_user_created_topic('jike')\n self.assertEqual(result, mock_topics)\n self.MockList.assert_called_once()\n mock_topics.load_more.assert_called_once()\n\n def test_get_user_subscribed_topic(self):\n mock_topics = Mock()\n self.MockList.return_value = mock_topics\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_user_subscribed_topic('jike')\n self.assertEqual(result, mock_topics)\n self.MockList.assert_called_once()\n mock_topics.load_more.assert_called_once()\n\n def test_get_user_following(self):\n mock_users = Mock()\n self.MockList.return_value = mock_users\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_user_following('jike')\n self.assertEqual(result, mock_users)\n self.MockList.assert_called_once()\n mock_users.load_more.assert_called_once()\n\n def test_get_user_follower(self):\n mock_users = Mock()\n self.MockList.return_value = mock_users\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_user_follower('jike')\n self.assertEqual(result, mock_users)\n self.MockList.assert_called_once()\n mock_users.load_more.assert_called_once()\n\n def test_get_comment(self):\n mock_message = Mock()\n mock_message.type = 'OFFICIAL_MESSAGE'\n mock_message.id = '123'\n\n mock_comments = Mock()\n self.MockStream.return_value = mock_comments\n self.MockStream.load_more.return_value = None\n result = self.jike_client.get_comment(mock_message)\n self.assertEqual(result, mock_comments)\n self.MockStream.assert_called_once()\n mock_comments.load_more.assert_called_once()\n\n def test_get_topic_selected(self):\n mock_posts = Mock()\n self.MockStream.return_value = mock_posts\n self.MockStream.load_more.return_value = None\n result = self.jike_client.get_topic_selected('123')\n self.assertEqual(result, mock_posts)\n self.MockStream.assert_called_once()\n mock_posts.load_more.assert_called_once()\n\n def test_get_topic_square(self):\n mock_posts = Mock()\n self.MockStream.return_value = mock_posts\n self.MockStream.load_more.return_value = None\n result = self.jike_client.get_topic_square('123')\n self.assertEqual(result, mock_posts)\n self.MockStream.assert_called_once()\n mock_posts.load_more.assert_called_once()\n\n def test_open_in_browser(self):\n ojbk = 'https://ojbk.com/'\n # open url\n with patch('webbrowser.open') as cm:\n self.jike_client.open_in_browser(ojbk)\n cm.assert_called_once_with(ojbk)\n # open message of `namedtuple`\n message_namedtuple = Mock()\n message_namedtuple.linkInfo = {'linkUrl': ojbk}\n with patch('webbrowser.open') as cm:\n self.jike_client.open_in_browser(message_namedtuple)\n cm.assert_called_once_with(ojbk)\n # open message of `dict`\n with patch('webbrowser.open') as cm:\n self.jike_client.open_in_browser({'linkInfo': {'linkUrl': ojbk}})\n cm.assert_called_once_with(ojbk)\n # open message with 'content', which has urls in it\n urls = ['a', ojbk]\n message_namedtuple.content = 'abc'\n with patch('jike.client.extract_url', return_value=urls), \\\n patch('webbrowser.open') as cm:\n self.jike_client.open_in_browser(message_namedtuple)\n cm.assert_called_with(ojbk)\n # not a url\n with self.assertRaises(ValueError):\n self.jike_client.open_in_browser([])\n # not a valid url\n with self.assertRaises(ValueError):\n self.jike_client.open_in_browser('123')\n\n def test_create_my_post(self):\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True, 'data': {}}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client.create_my_post('jike')\n self.assertIsInstance(result, tuple)\n # failed by post no string content\n with self.assertRaises(AssertionError):\n self.jike_client.create_my_post(123)\n # failed call by post both link and picture at one time\n with self.assertRaises(ValueError):\n self.jike_client.create_my_post('jike', link='a', pictures='b')\n mock_response.reset_mock()\n # failed call by post failed\n mock_response.json.return_value = {'success': False}\n with self.assertRaises(RuntimeError):\n self.jike_client.create_my_post('jike')\n # failed call by server error\n mock_response.status_code = 401\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.create_my_post('jike')\n\n def test_delete_my_post(self):\n mock_message = Mock()\n mock_message.type = 'ORIGINAL_POST'\n mock_message.id = '123'\n\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client.delete_my_post(mock_message)\n self.assertTrue(result)\n # failed call by no post id provided\n with self.assertRaises(AssertionError):\n self.jike_client.delete_my_post(None)\n # failed call by server error\n mock_response.status_code = 403\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.delete_my_post(mock_message)\n\n def test__like_action(self):\n mock_message = Mock()\n mock_message.type = 'OFFICIAL_MESSAGE'\n mock_message.id = '123'\n\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client._like_action(mock_message, 'like_it')\n self.assertTrue(result)\n # failed call by assertion\n mock_message.type = ''\n with self.assertRaises(AssertionError):\n self.jike_client._like_action(mock_message, 'unlike_it')\n # failed by server error\n mock_message.type = 'ORIGINAL_POST'\n mock_response.status_code = 402\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client._like_action(mock_message, 'like_it')\n\n def test_like_it(self):\n with patch('jike.client.JikeClient._like_action', return_value=None) as cm:\n client = JikeClient()\n client.like_it('msg')\n cm.assert_called_once_with('msg', 'like_it')\n\n def test_unlike_it(self):\n with patch('jike.client.JikeClient._like_action', return_value=None) as cm:\n client = JikeClient()\n client.unlike_it('msg')\n cm.assert_called_once_with('msg', 'unlike_it')\n\n def test__collect_action(self):\n mock_message = Mock()\n mock_message.type = 'OFFICIAL_MESSAGE'\n mock_message.id = '123'\n\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client._collect_action(mock_message, 'collect_it')\n self.assertTrue(result)\n # failed call by assertion\n mock_message.type = ''\n with self.assertRaises(AssertionError):\n self.jike_client._collect_action(mock_message, 'uncollect_it')\n # failed by server error\n mock_message.type = 'ORIGINAL_POST'\n mock_response.status_code = 403\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client._collect_action(mock_message, 'collect_it')\n\n def test_collect_it(self):\n with patch('jike.client.JikeClient._collect_action', return_value=None) as cm:\n client = JikeClient()\n client.collect_it('msg')\n cm.assert_called_once_with('msg', 'collect_it')\n\n def test_uncollect_it(self):\n with patch('jike.client.JikeClient._collect_action', return_value=None) as cm:\n client = JikeClient()\n client.uncollect_it('msg')\n cm.assert_called_once_with('msg', 'uncollect_it')\n\n def test_repost_it(self):\n mock_message = Mock()\n mock_message.type = 'OFFICIAL_MESSAGE'\n mock_message.id = '123'\n\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True, 'data': {}}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client.repost_it('jike', mock_message)\n self.assertIsInstance(result, tuple)\n # failed by post no string content\n with self.assertRaises(AssertionError):\n self.jike_client.repost_it(123, mock_message)\n # failed call by assertion\n mock_message.type = ''\n with self.assertRaises(AssertionError):\n self.jike_client.repost_it('jike', mock_message)\n # failed call by post failed\n mock_message.type = 'ORIGINAL_POST'\n mock_response.json.return_value = {'success': False}\n with self.assertRaises(RuntimeError):\n self.jike_client.repost_it('jike', mock_message)\n # failed by server error\n mock_response.status_code = 403\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.repost_it('jike', mock_message)\n\n def test_comment_it(self):\n mock_message = Mock()\n mock_message.type = 'OFFICIAL_MESSAGE'\n mock_message.id = '123'\n\n mock_response = Mock()\n mock_response.status_code = 200\n mock_response.json.return_value = {'success': True, 'data': {}}\n mock_response.raise_for_status.return_value = None\n self.mock_jike_session.post.return_value = mock_response\n result = self.jike_client.comment_it('jike', mock_message)\n self.assertIsInstance(result, tuple)\n # failed by post no string content\n with self.assertRaises(AssertionError):\n self.jike_client.comment_it(123, mock_message)\n # failed call by assertion\n mock_message.type = ''\n with self.assertRaises(AssertionError):\n self.jike_client.comment_it('jike', mock_message)\n # failed call by post failed\n mock_message.type = 'ORIGINAL_POST'\n mock_response.json.return_value = {'success': False}\n with self.assertRaises(RuntimeError):\n self.jike_client.comment_it('jike', mock_message)\n # failed by server error\n mock_response.status_code = 404\n mock_response.raise_for_status.side_effect = requests.HTTPError()\n with self.assertRaises(requests.HTTPError):\n self.jike_client.comment_it('jike', mock_message)\n\n def test_search_topic(self):\n mock_topics = Mock()\n self.MockList.return_value = mock_topics\n self.MockList.load_more.return_value = None\n result = self.jike_client.search_topic('jike')\n self.assertEqual(result, mock_topics)\n self.MockList.assert_called_once()\n mock_topics.load_more.assert_called_once()\n\n def test_search_collection(self):\n mock_collections = Mock()\n self.MockList.return_value = mock_collections\n self.MockList.load_more.return_value = None\n result = self.jike_client.search_collection('guoguo')\n self.assertEqual(result, mock_collections)\n self.MockList.assert_called_once()\n mock_collections.load_more.assert_called_once()\n\n def test_get_recommended_topics(self):\n mock_topics = Mock()\n self.MockList.return_value = mock_topics\n self.MockList.load_more.return_value = None\n result = self.jike_client.get_recommended_topic()\n self.assertEqual(result, mock_topics)\n self.MockList.assert_called_once()\n mock_topics.load_more.assert_called_once()\n\n def test__create_new_jike_session(self):\n self.jike_client.auth_token = 'new_token'\n self.jike_client._create_new_jike_session()\n self.MockJikeSession.assert_called_with('new_token')\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_message.py", "content": "import unittest\nfrom jike.objects.message import *\n\n\nclass TestMessage(unittest.TestCase):\n def setUp(self):\n self.message = {'id': 'a', 'content': 'b'}\n\n def test_official_message(self):\n message = {'video': 'c'}\n message.update(self.message)\n official_message = OfficialMessage(**message)\n self.assertEqual(official_message.id, 'a')\n self.assertEqual(official_message.content, 'b')\n self.assertEqual(official_message.video, 'c')\n self.assertIsNone(official_message.type)\n\n def test_original_post(self):\n message = {'messageId': 'c'}\n message.update(self.message)\n original_post = OriginalPost(**message)\n self.assertEqual(original_post.id, 'a')\n self.assertEqual(original_post.content, 'b')\n self.assertEqual(original_post.messageId, 'c')\n self.assertIsNone(original_post.type)\n\n def test_repost(self):\n message = {'replyToComment': 'c'}\n message.update(self.message)\n repost = Repost(**message)\n self.assertEqual(repost.id, 'a')\n self.assertEqual(repost.content, 'b')\n self.assertEqual(repost.replyToComment, 'c')\n self.assertIsNone(repost.type)\n\n def test_question(self):\n message = {'title': 'c'}\n message.update(self.message)\n question = Question(**message)\n self.assertEqual(question.id, 'a')\n self.assertEqual(question.content, 'b')\n self.assertEqual(question.title, 'c')\n self.assertIsNone(question.type)\n\n def test_answer(self):\n message = {'question': 'c'}\n message.update(self.message)\n answer = Answer(**message)\n self.assertEqual(answer.id, 'a')\n self.assertEqual(answer.content, 'b')\n self.assertEqual(answer.question, 'c')\n self.assertIsNone(answer.type)\n\n def test_person_update_section(self):\n personal_update_section = PersonalUpdateSection(**{'id': 'a', 'items': 'b'})\n self.assertEqual(personal_update_section.id, 'a')\n self.assertEqual(personal_update_section.items, 'b')\n self.assertIsNone(personal_update_section.type)\n\n def test_personal_update(self):\n personal_update = PersonalUpdate(**{'id': 'a', 'action': 'b'})\n self.assertEqual(personal_update.id, 'a')\n self.assertEqual(personal_update.action, 'b')\n self.assertIsNone(personal_update.type)\n\n def test_comment(self):\n comment = Comment(**{'id': 'a', 'content': 'b', 'liked': True})\n self.assertEqual(comment.id, 'a')\n self.assertEqual(comment.content, 'b')\n self.assertTrue(comment.liked)\n self.assertIsNone(comment.type)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_qr_code.py", "content": "import qrcode\nimport unittest\nfrom unittest.mock import *\n\nfrom jike import qr_code\n\n\nclass TestJikeQrCode(unittest.TestCase):\n def setUp(self):\n self.qr = qrcode.QRCode()\n self.qr.add_data('JikeMetro')\n\n def test_make_qrcode(self):\n with patch('jike.qr_code.render2terminal', return_value=None), \\\n patch('jike.qr_code.render2browser', return_value=None), \\\n patch('jike.qr_code.render2viewer', return_value=None):\n result = qr_code.make_qrcode({'uuid': '123'})\n self.assertIsNone(result)\n\n with patch('jike.qr_code.render2terminal', return_value=None), \\\n patch('jike.qr_code.render2browser', return_value=None), \\\n patch('jike.qr_code.render2viewer', return_value=None), \\\n self.assertRaises(AssertionError):\n qr_code.make_qrcode({'uuid': '123'}, render_choice='illegal choice')\n\n @patch.object(qrcode.QRCode, 'print_tty')\n def test_render2terminal(self, mock_print_tty):\n result = qr_code.render2terminal(self.qr)\n self.assertIsNone(result)\n mock_print_tty.assert_called_once()\n\n def test_render2browser(self):\n m = mock_open()\n with patch('tempfile.mkstemp', return_value=(None, 'a.html')), \\\n patch('builtins.open', m), \\\n patch('webbrowser.open', return_value=None):\n result = qr_code.render2browser(self.qr)\n self.assertIsNone(result)\n m.assert_called_once_with('a.html', 'wt', encoding='utf-8')\n m().write.assert_called()\n\n def test_render2viewer(self):\n m = mock_open()\n with patch('tempfile.mkstemp', return_value=(None, 'a.png')), \\\n patch('builtins.open', m), \\\n patch('webbrowser.open', return_value=None):\n result = qr_code.render2viewer(self.qr)\n self.assertIsNone(result)\n m.assert_called_once_with('a.png', 'wb')\n m().write.assert_called()\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_session.py", "content": "import unittest\nimport requests\nimport responses\nfrom urllib.parse import urlencode\n\nfrom jike.session import JikeSession\n\n\nclass TestJikeSession(unittest.TestCase):\n def setUp(self):\n self.jike_session = JikeSession('token')\n\n def tearDown(self):\n del self.jike_session\n\n def test_init(self):\n self.assertIsInstance(self.jike_session.session, requests.Session)\n self.assertEqual(self.jike_session.token, 'token')\n self.assertEqual(self.jike_session.headers['x-jike-app-auth-jwt'], 'token')\n\n def test_repr(self):\n self.assertEqual(repr(self.jike_session), 'JikeSession(token...token)')\n\n @responses.activate\n def test_get(self):\n url = 'https://test/'\n params = {'a': 'b'}\n responses.add(responses.GET, url, status=200)\n self.jike_session.get(url, params=params)\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, url + '?' + urlencode(params))\n self.assertEqual(responses.calls[0].request.headers['x-jike-app-auth-jwt'], 'token')\n self.assertEqual(responses.calls[0].response.status_code, 200)\n\n @responses.activate\n def test_post(self):\n url = 'https://test/'\n params = {'a': 'b'}\n json = {'x': 'y'}\n responses.add(responses.POST, url, json=json, status=200)\n self.jike_session.post(url, params=params, json=json)\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, url + '?' + urlencode(params))\n self.assertEqual(responses.calls[0].request.headers['x-jike-app-auth-jwt'], 'token')\n self.assertEqual(responses.calls[0].response.status_code, 200)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_topic.py", "content": "import unittest\nfrom jike.objects.topic import Topic\n\n\nclass TestTopic(unittest.TestCase):\n def test_topic(self):\n topic = Topic(**{'id': 'a', 'topicId': 'b'})\n self.assertEqual(topic.id, 'a')\n self.assertEqual(topic.topicId, 'b')\n self.assertIsNone(topic.topicType)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_user.py", "content": "import unittest\nfrom jike.objects.user import User\n\n\nclass TestUser(unittest.TestCase):\n def test_user(self):\n user = User(**{'id': 'a', 'userId': 'b'})\n self.assertEqual(user.id, 'a')\n self.assertEqual(user.userId, 'b')\n self.assertIsNone(user.username)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_utils.py", "content": "import unittest\nimport responses\nimport requests\nfrom urllib.parse import urlencode\nfrom unittest.mock import *\n\nfrom jike import utils, constants\n\n\nclass TestJikeUtils(unittest.TestCase):\n def setUp(self):\n self.session = requests.Session()\n\n def tearDown(self):\n self.session.close()\n\n def test_read_token(self):\n mocked_metro_json = '{\"auth_token\": \"token\"}'\n with patch('os.path.exists', return_value=True), \\\n patch('builtins.open', mock_open(read_data=mocked_metro_json)):\n mock_token = utils.read_token()\n self.assertEqual(mock_token, 'token')\n\n def test_write_token(self):\n m = mock_open()\n with patch('builtins.open', m):\n utils.write_token('token')\n m.assert_called_once_with(constants.AUTH_TOKEN_STORE_PATH, 'wt', encoding='utf-8')\n handle = m()\n handle.write.assert_called()\n\n @responses.activate\n def test_extract_link(self):\n success_response = {'success': True, 'data': 'link'}\n responses.add(responses.POST, constants.ENDPOINTS['extract_link'],\n json=success_response,\n status=200)\n responses.add(responses.POST, constants.ENDPOINTS['extract_link'],\n status=401)\n # success call\n result = utils.extract_link(self.session, 'https://www.ojbk.com')\n self.assertEqual(result, 'link')\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, constants.ENDPOINTS['extract_link'])\n self.assertEqual(responses.calls[0].response.json(), success_response)\n # failed call\n with self.assertRaises(requests.HTTPError) as cm:\n utils.extract_link(self.session, 'https://www.ojbk.com')\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(cm.exception.response.status_code, 401)\n\n def test_extract_url(self):\n content = 'no url inside'\n self.assertEqual(utils.extract_url(content), [])\n content = 'one url inside: https://www.jike.ojbk.com/metro end this url'\n self.assertEqual(utils.extract_url(content), ['https://www.jike.ojbk.com/metro'])\n content = 'more urls ' + content + ' http://www.test.xyz/a/132-z/ end '\n self.assertEqual(utils.extract_url(content),\n ['https://www.jike.ojbk.com/metro', 'http://www.test.xyz/a/132-z/'])\n\n @responses.activate\n def test_wait_login(self):\n success_response = {'logged_in': True}\n responses.add(responses.GET, constants.ENDPOINTS['wait_login'],\n json=success_response, status=200)\n failed_response = {'logged_in': False}\n responses.add(responses.GET, constants.ENDPOINTS['wait_login'],\n json=failed_response, status=200)\n responses.add(responses.GET, constants.ENDPOINTS['wait_login'],\n status=500)\n uuid = {'uuid': '123'}\n # success call\n result = utils.wait_login(uuid)\n self.assertTrue(result)\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, constants.ENDPOINTS['wait_login'] + '?' + urlencode(uuid))\n self.assertEqual(responses.calls[0].response.json(), success_response)\n # failed call\n result = utils.wait_login(uuid)\n self.assertFalse(result)\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(responses.calls[1].request.url, constants.ENDPOINTS['wait_login'] + '?' + urlencode(uuid))\n self.assertEqual(responses.calls[1].response.json(), failed_response)\n # failed again call\n with self.assertRaises(requests.HTTPError) as cm:\n utils.wait_login(uuid)\n self.assertEqual(len(responses.calls), 3)\n self.assertEqual(cm.exception.response.status_code, 500)\n\n @responses.activate\n def test_confirm_login(self):\n success_response = {'confirmed': True, 'token': 'token'}\n responses.add(responses.GET, constants.ENDPOINTS['confirm_login'],\n json=success_response, status=200)\n failed_response = {'confirmed': False, 'token': 'token'}\n responses.add(responses.GET, constants.ENDPOINTS['confirm_login'],\n json=failed_response, status=200)\n responses.add(responses.GET, constants.ENDPOINTS['confirm_login'],\n status=502)\n uuid = {'uuid': '123'}\n # success call\n result = utils.confirm_login(uuid)\n self.assertEqual(result, 'token')\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, constants.ENDPOINTS['confirm_login'] + '?' + urlencode(uuid))\n self.assertEqual(responses.calls[0].response.json(), success_response)\n # failed call\n with self.assertRaises(SystemExit):\n utils.confirm_login(uuid)\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(responses.calls[1].request.url, constants.ENDPOINTS['confirm_login'] + '?' + urlencode(uuid))\n self.assertEqual(responses.calls[1].response.json(), failed_response)\n # failed again call\n with self.assertRaises(requests.HTTPError) as cm:\n utils.confirm_login(uuid)\n self.assertEqual(len(responses.calls), 3)\n self.assertEqual(cm.exception.response.status_code, 502)\n\n @responses.activate\n def test_login(self):\n uuid = {'uuid': '123'}\n responses.add(responses.GET, constants.ENDPOINTS['create_session'],\n json=uuid, status=200)\n responses.add(responses.GET, constants.ENDPOINTS['create_session'],\n status=400)\n responses.add(responses.GET, constants.ENDPOINTS['create_session'],\n json=uuid, status=200)\n responses.add(responses.GET, constants.ENDPOINTS['create_session'],\n json=uuid, status=200)\n # success call\n with patch('jike.utils.wait_login', return_value=True), \\\n patch('jike.utils.confirm_login', return_value='token'), \\\n patch('jike.utils.make_qrcode'):\n result = utils.login()\n self.assertEqual(result, 'token')\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, constants.ENDPOINTS['create_session'])\n self.assertEqual(responses.calls[0].response.json(), uuid)\n # failed call\n with patch('jike.utils.wait_login', return_value=True), \\\n patch('jike.utils.confirm_login', return_value='token'), \\\n patch('jike.utils.make_qrcode'), \\\n self.assertRaises(requests.HTTPError) as cm:\n utils.login()\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(cm.exception.response.status_code, 400)\n # failed call by `wait_login`\n with patch('jike.utils.wait_login', return_value=False), \\\n patch('jike.utils.confirm_login', return_value='token'), \\\n patch('jike.utils.make_qrcode'), \\\n self.assertRaises(SystemExit):\n utils.login()\n self.assertEqual(len(responses.calls), 3)\n # failed call by `confirm_login`\n with patch('jike.utils.wait_login', return_value=True), \\\n patch('jike.utils.confirm_login', return_value=None), \\\n patch('jike.utils.make_qrcode'), \\\n self.assertRaises(SystemExit):\n utils.login()\n self.assertEqual(len(responses.calls), 4)\n\n @responses.activate\n def test_get_uptoken(self):\n params = {'bucket': 'jike'}\n success_reponse = {'uptoken': 'token'}\n responses.add(responses.GET, constants.ENDPOINTS['picture_uptoken'],\n json=success_reponse, status=200)\n responses.add(responses.GET, constants.ENDPOINTS['picture_uptoken'],\n status=404)\n # success call\n result = utils.get_uptoken()\n self.assertEqual(result, 'token')\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url,\n constants.ENDPOINTS['picture_uptoken'] + '?' + urlencode(params))\n self.assertEqual(responses.calls[0].response.json(), success_reponse)\n # failed call\n with self.assertRaises(requests.HTTPError) as cm:\n utils.get_uptoken()\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(cm.exception.response.status_code, 404)\n\n @responses.activate\n def test_upload_a_picture(self):\n # picture not exists\n with patch('os.path.exists', return_value=False), \\\n self.assertRaises(AssertionError):\n utils.upload_a_picture('jike.png')\n # cannot figure out mimetype\n with patch('os.path.exists', return_value=True), \\\n patch('os.path.split', return_value=('a', 'b')), \\\n patch('mimetypes.guess_type', return_value=(None, None)), \\\n self.assertRaises(AssertionError):\n utils.upload_a_picture('jike.png')\n # not upload picture\n with patch('os.path.exists', return_value=True), \\\n self.assertRaises(ValueError):\n utils.upload_a_picture('a.txt')\n\n success_reponse = {'success': True, 'key': 'key'}\n responses.add(responses.POST, constants.ENDPOINTS['picture_upload'],\n json=success_reponse, status=200)\n failed_response = {'success': False}\n responses.add(responses.POST, constants.ENDPOINTS['picture_upload'],\n json=failed_response, status=200)\n responses.add(responses.POST, constants.ENDPOINTS['picture_upload'],\n status=401)\n # success call\n with patch('os.path.exists', return_value=True), \\\n patch('jike.utils.get_uptoken', return_value='token'), \\\n patch('builtins.open', mock_open(read_data='picture_content')):\n result = utils.upload_a_picture('jike.png')\n self.assertEqual(result, 'key')\n self.assertEqual(len(responses.calls), 1)\n self.assertEqual(responses.calls[0].request.url, constants.ENDPOINTS['picture_upload'])\n self.assertEqual(responses.calls[0].response.json(), success_reponse)\n self.assertTrue(responses.calls[0].request.headers['Content-Type'].startswith('multipart/form-data;'))\n self.assertTrue(b'jike.png' in responses.calls[0].request.body)\n # failed call\n with patch('os.path.exists', return_value=True), \\\n patch('jike.utils.get_uptoken', return_value='token'), \\\n patch('builtins.open', mock_open(read_data='picture_content')), \\\n self.assertRaises(RuntimeError):\n utils.upload_a_picture('jike.png')\n self.assertEqual(len(responses.calls), 2)\n self.assertEqual(responses.calls[1].response.json(), failed_response)\n # failed again call\n with patch('os.path.exists', return_value=True), \\\n patch('jike.utils.get_uptoken', return_value='token'), \\\n patch('builtins.open', mock_open(read_data='picture_content')), \\\n self.assertRaises(requests.HTTPError) as cm:\n utils.upload_a_picture('jike.png')\n self.assertEqual(len(responses.calls), 3)\n self.assertEqual(cm.exception.response.status_code, 401)\n\n def test_upload_pictures(self):\n with patch('jike.utils.upload_a_picture', return_value='a_url'):\n pic_urls = utils.upload_pictures('p.png')\n self.assertEqual(len(pic_urls), 1)\n with patch('jike.utils.upload_a_picture', return_value='a_url'):\n pic_urls = utils.upload_pictures(['p.png', 'q.png'])\n self.assertEqual(len(pic_urls), 2)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tests/test_wrapper.py", "content": "import unittest\nfrom collections import namedtuple\nfrom jike.objects.wrapper import *\n\n\nclass TestWrapper(unittest.TestCase):\n def setUp(self):\n self.Test = namedtuple('Test', ['id', 'content', 'other', 'none'])\n\n def test_repr_namedtuple(self):\n self.Test.__repr__ = repr_namedtuple\n test = self.Test(**{'id': 'a', 'content': 'b', 'other': 'c', 'none': None})\n self.assertEqual(repr(test), 'Test(id=a, content=b)')\n\n def test_str_namedtuple(self):\n self.Test.__str__ = str_namedtuple\n test = self.Test(**{'id': 'a', 'content': 'b', 'other': 'c', 'none': None})\n self.assertEqual(str(test), 'Test(id=a, content=b, other=c)')\n\n def test_namedtuple_with_defaults(self):\n Test = namedtuple_with_defaults(self.Test)\n test = Test(**{'id': 'a', 'content': 'b', 'other': 'c'})\n self.assertEqual(test.id, 'a')\n self.assertEqual(test.content, 'b')\n self.assertEqual(test.other, 'c')\n self.assertIsNone(test.none)\n\n\nif __name__ == '__main__':\n unittest.main()\n" }, { "path": "tox.ini", "content": "# tox (https://tox.readthedocs.io/) is a tool for running tests\n# in multiple virtualenvs. This configuration file will run the\n# test suite on all supported python versions. To use it, \"pip install tox\"\n# and then run \"tox\" from this directory.\n\n[tox]\nenvlist = py{34,35,36}\n\n[testenv]\ndeps =\n requests\n pillow\n qrcode\n responses\ncommands =\n python setup.py test\n" } ]