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train
Client.send_text_message
发送文本消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param content: 消息正文 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包
werobot/client.py
def send_text_message(self, user_id, content, kf_account=None): """ 发送文本消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param content: 消息正文 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ data = { "touser": us...
def send_text_message(self, user_id, content, kf_account=None): """ 发送文本消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param content: 消息正文 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ data = { "touser": us...
[ "发送文本消息。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L728-L749
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_image_message
发送图片消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 图片的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包
werobot/client.py
def send_image_message(self, user_id, media_id, kf_account=None): """ 发送图片消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 图片的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ ...
def send_image_message(self, user_id, media_id, kf_account=None): """ 发送图片消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 图片的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ ...
[ "发送图片消息。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L751-L772
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_voice_message
发送语音消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 发送的语音的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包
werobot/client.py
def send_voice_message(self, user_id, media_id, kf_account=None): """ 发送语音消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 发送的语音的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ ...
def send_voice_message(self, user_id, media_id, kf_account=None): """ 发送语音消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 发送的语音的媒体ID。 可以通过 :func:`upload_media` 上传。 :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ ...
[ "发送语音消息。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L774-L795
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_music_message
发送音乐消息。 注意如果你遇到了缩略图不能正常显示的问题, 不要慌张; 目前来看是微信服务器端的问题。 对此我们也无能为力 ( `#197 <https://github.com/whtsky/WeRoBot/issues/197>`_ ) :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param url: 音乐链接 :param hq_url: 高品质音乐链接,wifi环境优先使用该链接播放音乐 :param thumb_media_id: 缩略图的媒体ID。 可以通过 :fun...
werobot/client.py
def send_music_message( self, user_id, url, hq_url, thumb_media_id, title=None, description=None, kf_account=None ): """ 发送音乐消息。 注意如果你遇到了缩略图不能正常显示的问题, 不要慌张; 目前来看是微信服务器端的问题。 对此我们也无能为力 ( `#197 <https://github.com/whtsky/We...
def send_music_message( self, user_id, url, hq_url, thumb_media_id, title=None, description=None, kf_account=None ): """ 发送音乐消息。 注意如果你遇到了缩略图不能正常显示的问题, 不要慌张; 目前来看是微信服务器端的问题。 对此我们也无能为力 ( `#197 <https://github.com/whtsky/We...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L825-L865
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_article_message
发送图文消息:: articles = [ { "title":"Happy Day", "description":"Is Really A Happy Day", "url":"URL", "picurl":"PIC_URL" }, { "title":"Happy Day", ...
werobot/client.py
def send_article_message(self, user_id, articles, kf_account=None): """ 发送图文消息:: articles = [ { "title":"Happy Day", "description":"Is Really A Happy Day", "url":"URL", "picurl":"PIC_URL" ...
def send_article_message(self, user_id, articles, kf_account=None): """ 发送图文消息:: articles = [ { "title":"Happy Day", "description":"Is Really A Happy Day", "url":"URL", "picurl":"PIC_URL" ...
[ "发送图文消息", "::" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L867-L912
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_news_message
发送永久素材中的图文消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 媒体文件 ID :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包
werobot/client.py
def send_news_message(self, user_id, media_id, kf_account=None): """ 发送永久素材中的图文消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 媒体文件 ID :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ data = { "...
def send_news_message(self, user_id, media_id, kf_account=None): """ 发送永久素材中的图文消息。 :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param media_id: 媒体文件 ID :param kf_account: 发送消息的客服账户,默认值为 None,None 为不指定 :return: 返回的 JSON 数据包 """ data = { "...
[ "发送永久素材中的图文消息。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L914-L935
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_miniprogrampage_message
发送小程序卡片(要求小程序与公众号已关联) :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param title: 小程序卡片的标题 :param appid: 小程序的 appid,要求小程序的 appid 需要与公众号有关联关系 :param pagepath: 小程序的页面路径,跟 app.json 对齐,支持参数,比如 pages/index/index?foo=bar :param thumb_media_id: 小程序卡片图片的媒体 ID,小程序卡片图片建议大小为 520*416 ...
werobot/client.py
def send_miniprogrampage_message( self, user_id, title, appid, pagepath, thumb_media_id, kf_account=None ): """ 发送小程序卡片(要求小程序与公众号已关联) :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param title: 小程序卡片的标题 :param appid: 小程序的 appid,要求小程序的 appid 需要与公众号有关联关系 :p...
def send_miniprogrampage_message( self, user_id, title, appid, pagepath, thumb_media_id, kf_account=None ): """ 发送小程序卡片(要求小程序与公众号已关联) :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param title: 小程序卡片的标题 :param appid: 小程序的 appid,要求小程序的 appid 需要与公众号有关联关系 :p...
[ "发送小程序卡片(要求小程序与公众号已关联)" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L937-L966
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.send_template_message
发送模板消息 详情请参考 http://mp.weixin.qq.com/wiki/17/304c1885ea66dbedf7dc170d84999a9d.html :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param template_id: 模板 ID。 :param data: 用于渲染模板的数据。 :param url: 模板消息的可选链接。 :return: 返回的 JSON 数据包
werobot/client.py
def send_template_message(self, user_id, template_id, data, url=''): """ 发送模板消息 详情请参考 http://mp.weixin.qq.com/wiki/17/304c1885ea66dbedf7dc170d84999a9d.html :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param template_id: 模板 ID。 :param data: 用于渲染模板的数据。 :param...
def send_template_message(self, user_id, template_id, data, url=''): """ 发送模板消息 详情请参考 http://mp.weixin.qq.com/wiki/17/304c1885ea66dbedf7dc170d84999a9d.html :param user_id: 用户 ID 。 就是你收到的 `Message` 的 source :param template_id: 模板 ID。 :param data: 用于渲染模板的数据。 :param...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L991-L1010
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.update_tag
修改标签 :param tag_id: 标签 ID :param tag_name: 新的标签名 :return: 返回的 JSON 数据包
werobot/client.py
def update_tag(self, tag_id, tag_name): """ 修改标签 :param tag_id: 标签 ID :param tag_name: 新的标签名 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/update", data={"tag": { "id": tag_id, ...
def update_tag(self, tag_id, tag_name): """ 修改标签 :param tag_id: 标签 ID :param tag_name: 新的标签名 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/update", data={"tag": { "id": tag_id, ...
[ "修改标签" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L1034-L1048
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.get_users_by_tag
获取标签下粉丝列表 :param tag_id: 标签 ID :param next_open_id: 第一个拉取用户的 OPENID,默认从头开始拉取 :return: 返回的 JSON 数据包
werobot/client.py
def get_users_by_tag(self, tag_id, next_open_id=""): """ 获取标签下粉丝列表 :param tag_id: 标签 ID :param next_open_id: 第一个拉取用户的 OPENID,默认从头开始拉取 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/user/tag/get", data={ ...
def get_users_by_tag(self, tag_id, next_open_id=""): """ 获取标签下粉丝列表 :param tag_id: 标签 ID :param next_open_id: 第一个拉取用户的 OPENID,默认从头开始拉取 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/user/tag/get", data={ ...
[ "获取标签下粉丝列表" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L1064-L1078
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.tag_users
批量为用户打标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包
werobot/client.py
def tag_users(self, tag_id, open_id_list): """ 批量为用户打标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/members/batchtagging", data={ ...
def tag_users(self, tag_id, open_id_list): """ 批量为用户打标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/members/batchtagging", data={ ...
[ "批量为用户打标签" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L1094-L1108
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Client.untag_users
批量为用户取消标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包
werobot/client.py
def untag_users(self, tag_id, open_id_list): """ 批量为用户取消标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/members/batchuntagging", dat...
def untag_users(self, tag_id, open_id_list): """ 批量为用户取消标签 :param tag_id: 标签 ID :param open_id_list: 包含一个或多个用户的 OPENID 的列表 :return: 返回的 JSON 数据包 """ return self.post( url="https://api.weixin.qq.com/cgi-bin/tags/members/batchuntagging", dat...
[ "批量为用户取消标签" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/client.py#L1110-L1124
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
FileStorage.get
根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象
werobot/session/filestorage.py
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ try: session_json = self.db[id] except KeyError: session_json = "{}" return json_loads(session_json)
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ try: session_json = self.db[id] except KeyError: session_json = "{}" return json_loads(session_json)
[ "根据", "id", "获取数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/filestorage.py#L28-L39
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
PrpCrypto.encrypt
对明文进行加密 :param text: 需要加密的明文 :param app_id: 微信公众平台的 AppID :return: 加密后的字符串
werobot/crypto/__init__.py
def encrypt(self, text, app_id): """ 对明文进行加密 :param text: 需要加密的明文 :param app_id: 微信公众平台的 AppID :return: 加密后的字符串 """ text = b"".join( [ to_binary(self.get_random_string()), struct.pack(b"I", socket.htonl(len(to_binary(tex...
def encrypt(self, text, app_id): """ 对明文进行加密 :param text: 需要加密的明文 :param app_id: 微信公众平台的 AppID :return: 加密后的字符串 """ text = b"".join( [ to_binary(self.get_random_string()), struct.pack(b"I", socket.htonl(len(to_binary(tex...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/crypto/__init__.py#L45-L63
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
PrpCrypto.decrypt
对密文进行解密 :param text: 需要解密的密文 :param app_id: 微信公众平台的 AppID :return: 解密后的字符串
werobot/crypto/__init__.py
def decrypt(self, text, app_id): """ 对密文进行解密 :param text: 需要解密的密文 :param app_id: 微信公众平台的 AppID :return: 解密后的字符串 """ text = to_binary(text) decryptor = self.cipher.decryptor() plain_text = decryptor.update(base64.b64decode(text) ...
def decrypt(self, text, app_id): """ 对密文进行解密 :param text: 需要解密的密文 :param app_id: 微信公众平台的 AppID :return: 解密后的字符串 """ text = to_binary(text) decryptor = self.cipher.decryptor() plain_text = decryptor.update(base64.b64decode(text) ...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/crypto/__init__.py#L65-L87
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
MessageCrypt.decrypt_message
解密收到的微信消息 :param timestamp: 请求 URL 中收到的 timestamp :param nonce: 请求 URL 中收到的 nonce :param msg_signature: 请求 URL 中收到的 msg_signature :param encrypt_msg: 收到的加密文本. ( XML 中的 <Encrypt> 部分 ) :return: 解密后的 XML 文本
werobot/crypto/__init__.py
def decrypt_message(self, timestamp, nonce, msg_signature, encrypt_msg): """ 解密收到的微信消息 :param timestamp: 请求 URL 中收到的 timestamp :param nonce: 请求 URL 中收到的 nonce :param msg_signature: 请求 URL 中收到的 msg_signature :param encrypt_msg: 收到的加密文本. ( XML 中的 <Encrypt> 部分 ) :ret...
def decrypt_message(self, timestamp, nonce, msg_signature, encrypt_msg): """ 解密收到的微信消息 :param timestamp: 请求 URL 中收到的 timestamp :param nonce: 请求 URL 中收到的 nonce :param msg_signature: 请求 URL 中收到的 msg_signature :param encrypt_msg: 收到的加密文本. ( XML 中的 <Encrypt> 部分 ) :ret...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/crypto/__init__.py#L109-L121
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
MessageCrypt.encrypt_message
加密微信回复 :param reply: 加密前的回复 :type reply: WeChatReply 或 XML 文本 :return: 加密后的回复文本
werobot/crypto/__init__.py
def encrypt_message(self, reply, timestamp=None, nonce=None): """ 加密微信回复 :param reply: 加密前的回复 :type reply: WeChatReply 或 XML 文本 :return: 加密后的回复文本 """ if hasattr(reply, "render"): reply = reply.render() timestamp = timestamp or to_text(int(time...
def encrypt_message(self, reply, timestamp=None, nonce=None): """ 加密微信回复 :param reply: 加密前的回复 :type reply: WeChatReply 或 XML 文本 :return: 加密后的回复文本 """ if hasattr(reply, "render"): reply = reply.render() timestamp = timestamp or to_text(int(time...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/crypto/__init__.py#L123-L144
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
SaeKVDBStorage.set
根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象
werobot/session/saekvstorage.py
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ return self.kv.set(self.key_name(id), value)
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ return self.kv.set(self.key_name(id), value)
[ "根据", "id", "写入数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/saekvstorage.py#L42-L49
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
make_view
为一个 BaseRoBot 生成 Django view。 :param robot: 一个 BaseRoBot 实例。 :return: 一个标准的 Django view
werobot/contrib/django.py
def make_view(robot): """ 为一个 BaseRoBot 生成 Django view。 :param robot: 一个 BaseRoBot 实例。 :return: 一个标准的 Django view """ @csrf_exempt def werobot_view(request): timestamp = request.GET.get("timestamp", "") nonce = request.GET.get("nonce", "") signature = request.GET.ge...
def make_view(robot): """ 为一个 BaseRoBot 生成 Django view。 :param robot: 一个 BaseRoBot 实例。 :return: 一个标准的 Django view """ @csrf_exempt def werobot_view(request): timestamp = request.GET.get("timestamp", "") nonce = request.GET.get("nonce", "") signature = request.GET.ge...
[ "为一个", "BaseRoBot", "生成", "Django", "view。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/contrib/django.py#L13-L50
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.create_js_pay_package
签名 pay package 需要的参数 详情请参考 支付开发文档 :param package: 需要签名的的参数 :return: 可以使用的packagestr
werobot/pay.py
def create_js_pay_package(self, **package): """ 签名 pay package 需要的参数 详情请参考 支付开发文档 :param package: 需要签名的的参数 :return: 可以使用的packagestr """ assert self.pay_partner_id, "PAY_PARTNER_ID IS EMPTY" assert self.pay_partner_key, "PAY_PARTNER_KEY IS EMPTY" ...
def create_js_pay_package(self, **package): """ 签名 pay package 需要的参数 详情请参考 支付开发文档 :param package: 需要签名的的参数 :return: 可以使用的packagestr """ assert self.pay_partner_id, "PAY_PARTNER_ID IS EMPTY" assert self.pay_partner_key, "PAY_PARTNER_KEY IS EMPTY" ...
[ "签名", "pay", "package", "需要的参数", "详情请参考", "支付开发文档" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L27-L58
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.create_js_pay_params
签名 js 需要的参数 详情请参考 支付开发文档 :: wxclient.create_js_pay_params( body=标题, out_trade_no=本地订单号, total_fee=价格单位分, notify_url=通知url, spbill_create_ip=建议为支付人ip, ) :param package: 需要签名的的参数 :return: 支付需要的对象
werobot/pay.py
def create_js_pay_params(self, **package): """ 签名 js 需要的参数 详情请参考 支付开发文档 :: wxclient.create_js_pay_params( body=标题, out_trade_no=本地订单号, total_fee=价格单位分, notify_url=通知url, spbill_create_ip=建议为支付人ip, ) :param...
def create_js_pay_params(self, **package): """ 签名 js 需要的参数 详情请参考 支付开发文档 :: wxclient.create_js_pay_params( body=标题, out_trade_no=本地订单号, total_fee=价格单位分, notify_url=通知url, spbill_create_ip=建议为支付人ip, ) :param...
[ "签名", "js", "需要的参数", "详情请参考", "支付开发文档" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L60-L86
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.create_js_edit_address_param
alpha 暂时不建议使用 这个接口使用起来十分不友好 而且会引起巨大的误解 url 需要带上 code 和 state (url?code=xxx&state=1) code 和state 是 oauth 时候回来的 token 要传用户的 token 这尼玛 你能相信这些支付接口都是腾讯出的?
werobot/pay.py
def create_js_edit_address_param(self, accesstoken, **params): """ alpha 暂时不建议使用 这个接口使用起来十分不友好 而且会引起巨大的误解 url 需要带上 code 和 state (url?code=xxx&state=1) code 和state 是 oauth 时候回来的 token 要传用户的 token 这尼玛 你能相信这些支付接口都是腾讯出的? """ params.u...
def create_js_edit_address_param(self, accesstoken, **params): """ alpha 暂时不建议使用 这个接口使用起来十分不友好 而且会引起巨大的误解 url 需要带上 code 和 state (url?code=xxx&state=1) code 和state 是 oauth 时候回来的 token 要传用户的 token 这尼玛 你能相信这些支付接口都是腾讯出的? """ params.u...
[ "alpha", "暂时不建议使用", "这个接口使用起来十分不友好", "而且会引起巨大的误解" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L88-L123
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.create_native_pay_url
创建 native pay url 详情请参考 支付开发文档 :param productid: 本地商品ID :return: 返回URL
werobot/pay.py
def create_native_pay_url(self, productid): """ 创建 native pay url 详情请参考 支付开发文档 :param productid: 本地商品ID :return: 返回URL """ params, sign, = self._pay_sign_dict(productid=productid) params['sign'] = sign return NATIVE_BASE_URL + urlencode(params)
def create_native_pay_url(self, productid): """ 创建 native pay url 详情请参考 支付开发文档 :param productid: 本地商品ID :return: 返回URL """ params, sign, = self._pay_sign_dict(productid=productid) params['sign'] = sign return NATIVE_BASE_URL + urlencode(params)
[ "创建", "native", "pay", "url", "详情请参考", "支付开发文档" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L125-L138
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.pay_deliver_notify
通知 腾讯发货 一般形式 :: wxclient.pay_delivernotify( openid=openid, transid=transaction_id, out_trade_no=本地订单号, deliver_timestamp=int(time.time()), deliver_status="1", deliver_msg="ok" ) :par...
werobot/pay.py
def pay_deliver_notify(self, **deliver_info): """ 通知 腾讯发货 一般形式 :: wxclient.pay_delivernotify( openid=openid, transid=transaction_id, out_trade_no=本地订单号, deliver_timestamp=int(time.time()), deliver_status...
def pay_deliver_notify(self, **deliver_info): """ 通知 腾讯发货 一般形式 :: wxclient.pay_delivernotify( openid=openid, transid=transaction_id, out_trade_no=本地订单号, deliver_timestamp=int(time.time()), deliver_status...
[ "通知", "腾讯发货" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L140-L166
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeixinPayClient.pay_order_query
查询订单状态 一般用于无法确定 订单状态时候补偿 :param out_trade_no: 本地订单号 :return: 订单信息dict
werobot/pay.py
def pay_order_query(self, out_trade_no): """ 查询订单状态 一般用于无法确定 订单状态时候补偿 :param out_trade_no: 本地订单号 :return: 订单信息dict """ package = { 'partner': self.pay_partner_id, 'out_trade_no': out_trade_no, } _package = package.items()...
def pay_order_query(self, out_trade_no): """ 查询订单状态 一般用于无法确定 订单状态时候补偿 :param out_trade_no: 本地订单号 :return: 订单信息dict """ package = { 'partner': self.pay_partner_id, 'out_trade_no': out_trade_no, } _package = package.items()...
[ "查询订单状态", "一般用于无法确定", "订单状态时候补偿" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/pay.py#L168-L204
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
byte2int
Get the ASCII int value of a character in a string. :param s: a string :param index: the position of desired character :return: ASCII int value
werobot/utils.py
def byte2int(s, index=0): """Get the ASCII int value of a character in a string. :param s: a string :param index: the position of desired character :return: ASCII int value """ if six.PY2: return ord(s[index]) return s[index]
def byte2int(s, index=0): """Get the ASCII int value of a character in a string. :param s: a string :param index: the position of desired character :return: ASCII int value """ if six.PY2: return ord(s[index]) return s[index]
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/utils.py#L76-L86
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
pay_sign_dict
支付参数签名
werobot/utils.py
def pay_sign_dict( appid, pay_sign_key, add_noncestr=True, add_timestamp=True, add_appid=True, **kwargs ): """ 支付参数签名 """ assert pay_sign_key, "PAY SIGN KEY IS EMPTY" if add_appid: kwargs.update({'appid': appid}) if add_noncestr: kwargs.update({'noncestr...
def pay_sign_dict( appid, pay_sign_key, add_noncestr=True, add_timestamp=True, add_appid=True, **kwargs ): """ 支付参数签名 """ assert pay_sign_key, "PAY SIGN KEY IS EMPTY" if add_appid: kwargs.update({'appid': appid}) if add_noncestr: kwargs.update({'noncestr...
[ "支付参数签名" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/utils.py#L107-L142
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.key_click
为自定义菜单 ``(click)`` 事件添加 handler 的简便方法。 **@key_click('KEYNAME')** 用来为特定 key 的点击事件添加 handler 方法。
werobot/robot.py
def key_click(self, key): """ 为自定义菜单 ``(click)`` 事件添加 handler 的简便方法。 **@key_click('KEYNAME')** 用来为特定 key 的点击事件添加 handler 方法。 """ def wraps(f): argc = len(signature(f).parameters.keys()) @self.click def onclick(message, session=None): ...
def key_click(self, key): """ 为自定义菜单 ``(click)`` 事件添加 handler 的简便方法。 **@key_click('KEYNAME')** 用来为特定 key 的点击事件添加 handler 方法。 """ def wraps(f): argc = len(signature(f).parameters.keys()) @self.click def onclick(message, session=None): ...
[ "为自定义菜单", "(", "click", ")", "事件添加", "handler", "的简便方法。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L467-L484
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.filter
为文本 ``(text)`` 消息添加 handler 的简便方法。 使用 ``@filter("xxx")``, ``@filter(re.compile("xxx"))`` 或 ``@filter("xxx", "xxx2")`` 的形式为特定内容添加 handler。
werobot/robot.py
def filter(self, *args): """ 为文本 ``(text)`` 消息添加 handler 的简便方法。 使用 ``@filter("xxx")``, ``@filter(re.compile("xxx"))`` 或 ``@filter("xxx", "xxx2")`` 的形式为特定内容添加 handler。 """ def wraps(f): self.add_filter(func=f, rules=list(args)) return f r...
def filter(self, *args): """ 为文本 ``(text)`` 消息添加 handler 的简便方法。 使用 ``@filter("xxx")``, ``@filter(re.compile("xxx"))`` 或 ``@filter("xxx", "xxx2")`` 的形式为特定内容添加 handler。 """ def wraps(f): self.add_filter(func=f, rules=list(args)) return f r...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L486-L498
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.add_handler
为 BaseRoBot 实例添加一个 handler。 :param func: 要作为 handler 的方法。 :param type: handler 的种类。 :return: None
werobot/robot.py
def add_handler(self, func, type='all'): """ 为 BaseRoBot 实例添加一个 handler。 :param func: 要作为 handler 的方法。 :param type: handler 的种类。 :return: None """ if not callable(func): raise ValueError("{} is not callable".format(func)) self._handlers[type]...
def add_handler(self, func, type='all'): """ 为 BaseRoBot 实例添加一个 handler。 :param func: 要作为 handler 的方法。 :param type: handler 的种类。 :return: None """ if not callable(func): raise ValueError("{} is not callable".format(func)) self._handlers[type]...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L500-L513
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.add_filter
为 BaseRoBot 添加一个 ``filter handler``。 :param func: 如果 rules 通过,则处理该消息的 handler。 :param rules: 一个 list,包含要匹配的字符串或者正则表达式。 :return: None
werobot/robot.py
def add_filter(self, func, rules): """ 为 BaseRoBot 添加一个 ``filter handler``。 :param func: 如果 rules 通过,则处理该消息的 handler。 :param rules: 一个 list,包含要匹配的字符串或者正则表达式。 :return: None """ if not callable(func): raise ValueError("{} is not callable".format(func)) ...
def add_filter(self, func, rules): """ 为 BaseRoBot 添加一个 ``filter handler``。 :param func: 如果 rules 通过,则处理该消息的 handler。 :param rules: 一个 list,包含要匹配的字符串或者正则表达式。 :return: None """ if not callable(func): raise ValueError("{} is not callable".format(func)) ...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L518-L554
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.parse_message
解析获取到的 Raw XML ,如果需要的话进行解密,返回 WeRoBot Message。 :param body: 微信服务器发来的请求中的 Body。 :return: WeRoBot Message
werobot/robot.py
def parse_message( self, body, timestamp=None, nonce=None, msg_signature=None ): """ 解析获取到的 Raw XML ,如果需要的话进行解密,返回 WeRoBot Message。 :param body: 微信服务器发来的请求中的 Body。 :return: WeRoBot Message """ message_dict = parse_xml(body) if "Encrypt" in message_dict...
def parse_message( self, body, timestamp=None, nonce=None, msg_signature=None ): """ 解析获取到的 Raw XML ,如果需要的话进行解密,返回 WeRoBot Message。 :param body: 微信服务器发来的请求中的 Body。 :return: WeRoBot Message """ message_dict = parse_xml(body) if "Encrypt" in message_dict...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L556-L573
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.get_reply
根据 message 的内容获取 Reply 对象。 :param message: 要处理的 message :return: 获取的 Reply 对象
werobot/robot.py
def get_reply(self, message): """ 根据 message 的内容获取 Reply 对象。 :param message: 要处理的 message :return: 获取的 Reply 对象 """ session_storage = self.session_storage id = None session = None if session_storage and hasattr(message, "source"): id ...
def get_reply(self, message): """ 根据 message 的内容获取 Reply 对象。 :param message: 要处理的 message :return: 获取的 Reply 对象 """ session_storage = self.session_storage id = None session = None if session_storage and hasattr(message, "source"): id ...
[ "根据", "message", "的内容获取", "Reply", "对象。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L575-L600
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.get_encrypted_reply
对一个指定的 WeRoBot Message ,获取 handlers 处理后得到的 Reply。 如果可能,对该 Reply 进行加密。 返回 Reply Render 后的文本。 :param message: 一个 WeRoBot Message 实例。 :return: reply (纯文本)
werobot/robot.py
def get_encrypted_reply(self, message): """ 对一个指定的 WeRoBot Message ,获取 handlers 处理后得到的 Reply。 如果可能,对该 Reply 进行加密。 返回 Reply Render 后的文本。 :param message: 一个 WeRoBot Message 实例。 :return: reply (纯文本) """ reply = self.get_reply(message) if not reply: ...
def get_encrypted_reply(self, message): """ 对一个指定的 WeRoBot Message ,获取 handlers 处理后得到的 Reply。 如果可能,对该 Reply 进行加密。 返回 Reply Render 后的文本。 :param message: 一个 WeRoBot Message 实例。 :return: reply (纯文本) """ reply = self.get_reply(message) if not reply: ...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L602-L618
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
BaseRoBot.check_signature
根据时间戳和生成签名的字符串 (nonce) 检查签名。 :param timestamp: 时间戳 :param nonce: 生成签名的随机字符串 :param signature: 要检查的签名 :return: 如果签名合法将返回 ``True``,不合法将返回 ``False``
werobot/robot.py
def check_signature(self, timestamp, nonce, signature): """ 根据时间戳和生成签名的字符串 (nonce) 检查签名。 :param timestamp: 时间戳 :param nonce: 生成签名的随机字符串 :param signature: 要检查的签名 :return: 如果签名合法将返回 ``True``,不合法将返回 ``False`` """ return check_signature( self.conf...
def check_signature(self, timestamp, nonce, signature): """ 根据时间戳和生成签名的字符串 (nonce) 检查签名。 :param timestamp: 时间戳 :param nonce: 生成签名的随机字符串 :param signature: 要检查的签名 :return: 如果签名合法将返回 ``True``,不合法将返回 ``False`` """ return check_signature( self.conf...
[ "根据时间戳和生成签名的字符串", "(", "nonce", ")", "检查签名。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L620-L631
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
WeRoBot.run
运行 WeRoBot。 :param server: 传递给 Bottle 框架 run 方法的参数,详情见\ `bottle 文档 <https://bottlepy.org/docs/dev/deployment.html#switching-the-server-backend>`_ :param host: 运行时绑定的主机地址 :param port: 运行时绑定的主机端口 :param enable_pretty_logging: 是否开启 log 的输出格式优化
werobot/robot.py
def run( self, server=None, host=None, port=None, enable_pretty_logging=True ): """ 运行 WeRoBot。 :param server: 传递给 Bottle 框架 run 方法的参数,详情见\ `bottle 文档 <https://bottlepy.org/docs/dev/deployment.html#switching-the-server-backend>`_ :param host: 运行时绑定的主机地址 :para...
def run( self, server=None, host=None, port=None, enable_pretty_logging=True ): """ 运行 WeRoBot。 :param server: 传递给 Bottle 框架 run 方法的参数,详情见\ `bottle 文档 <https://bottlepy.org/docs/dev/deployment.html#switching-the-server-backend>`_ :param host: 运行时绑定的主机地址 :para...
[ "运行", "WeRoBot。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/robot.py#L665-L689
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
PostgreSQLStorage.get
根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象
werobot/session/postgresqlstorage.py
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ cur = self.conn.cursor() cur.execute("SELECT value FROM WeRoBot WHERE id=%s LIMIT 1;", (id, )) session_json = cur.fetchone() if session_json is N...
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ cur = self.conn.cursor() cur.execute("SELECT value FROM WeRoBot WHERE id=%s LIMIT 1;", (id, )) session_json = cur.fetchone() if session_json is N...
[ "根据", "id", "获取数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/postgresqlstorage.py#L41-L53
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
PostgreSQLStorage.delete
根据 id 删除数据。 :param id: 要删除的数据的 id
werobot/session/postgresqlstorage.py
def delete(self, id): """ 根据 id 删除数据。 :param id: 要删除的数据的 id """ self.conn.cursor().execute("DELETE FROM WeRoBot WHERE id=%s", (id, )) self.conn.commit()
def delete(self, id): """ 根据 id 删除数据。 :param id: 要删除的数据的 id """ self.conn.cursor().execute("DELETE FROM WeRoBot WHERE id=%s", (id, )) self.conn.commit()
[ "根据", "id", "删除数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/postgresqlstorage.py#L73-L80
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
make_view
为一个 BaseRoBot 生成 Bottle view。 Usage :: from werobot import WeRoBot robot = WeRoBot(token='token') @robot.handler def hello(message): return 'Hello World!' from bottle import Bottle from werobot.contrib.bottle import make_view app = Bottle() ...
werobot/contrib/bottle.py
def make_view(robot): """ 为一个 BaseRoBot 生成 Bottle view。 Usage :: from werobot import WeRoBot robot = WeRoBot(token='token') @robot.handler def hello(message): return 'Hello World!' from bottle import Bottle from werobot.contrib.bottle import ...
def make_view(robot): """ 为一个 BaseRoBot 生成 Bottle view。 Usage :: from werobot import WeRoBot robot = WeRoBot(token='token') @robot.handler def hello(message): return 'Hello World!' from bottle import Bottle from werobot.contrib.bottle import ...
[ "为一个", "BaseRoBot", "生成", "Bottle", "view。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/contrib/bottle.py#L12-L63
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
process_message
Process a message dict and return a Message Object :param message: Message dict returned by `parse_xml` function :return: Message Object
werobot/parser.py
def process_message(message): """ Process a message dict and return a Message Object :param message: Message dict returned by `parse_xml` function :return: Message Object """ message["type"] = message.pop("MsgType").lower() if message["type"] == 'event': message["type"] = str(message...
def process_message(message): """ Process a message dict and return a Message Object :param message: Message dict returned by `parse_xml` function :return: Message Object """ message["type"] = message.pop("MsgType").lower() if message["type"] == 'event': message["type"] = str(message...
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offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/parser.py#L20-L34
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
MySQLStorage.set
根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象
werobot/session/mysqlstorage.py
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ value = json_dumps(value) self.conn.cursor().execute( "INSERT INTO WeRoBot (id, value) VALUES (%s,%s) \ ON DUPLICATE KEY UPDATE val...
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ value = json_dumps(value) self.conn.cursor().execute( "INSERT INTO WeRoBot (id, value) VALUES (%s,%s) \ ON DUPLICATE KEY UPDATE val...
[ "根据", "id", "写入数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/mysqlstorage.py#L72-L88
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
RedisStorage.get
根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象
werobot/session/redisstorage.py
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ id = self.key_name(id) session_json = self.redis.get(id) or '{}' return json_loads(session_json)
def get(self, id): """ 根据 id 获取数据。 :param id: 要获取的数据的 id :return: 返回取到的数据,如果是空则返回一个空的 ``dict`` 对象 """ id = self.key_name(id) session_json = self.redis.get(id) or '{}' return json_loads(session_json)
[ "根据", "id", "获取数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/redisstorage.py#L36-L45
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
RedisStorage.set
根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象
werobot/session/redisstorage.py
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ id = self.key_name(id) self.redis.set(id, json_dumps(value))
def set(self, id, value): """ 根据 id 写入数据。 :param id: 要写入的 id :param value: 要写入的数据,可以是一个 ``dict`` 对象 """ id = self.key_name(id) self.redis.set(id, json_dumps(value))
[ "根据", "id", "写入数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/redisstorage.py#L47-L55
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
RedisStorage.delete
根据 id 删除数据。 :param id: 要删除的数据的 id
werobot/session/redisstorage.py
def delete(self, id): """ 根据 id 删除数据。 :param id: 要删除的数据的 id """ id = self.key_name(id) self.redis.delete(id)
def delete(self, id): """ 根据 id 删除数据。 :param id: 要删除的数据的 id """ id = self.key_name(id) self.redis.delete(id)
[ "根据", "id", "删除数据。" ]
offu/WeRoBot
python
https://github.com/offu/WeRoBot/blob/fd42109105b03f9acf45ebd9dcabb9d5cff98f3c/werobot/session/redisstorage.py#L57-L64
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fd42109105b03f9acf45ebd9dcabb9d5cff98f3c
train
Vocab.load_vectors
Arguments: vectors: one of or a list containing instantiations of the GloVe, CharNGram, or Vectors classes. Alternatively, one of or a list of available pretrained vectors: charngram.100d fasttext.en.300d fasttext.simple.300d ...
torchtext/vocab.py
def load_vectors(self, vectors, **kwargs): """ Arguments: vectors: one of or a list containing instantiations of the GloVe, CharNGram, or Vectors classes. Alternatively, one of or a list of available pretrained vectors: charngram.100d ...
def load_vectors(self, vectors, **kwargs): """ Arguments: vectors: one of or a list containing instantiations of the GloVe, CharNGram, or Vectors classes. Alternatively, one of or a list of available pretrained vectors: charngram.100d ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/vocab.py#L117-L165
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Vocab.set_vectors
Set the vectors for the Vocab instance from a collection of Tensors. Arguments: stoi: A dictionary of string to the index of the associated vector in the `vectors` input argument. vectors: An indexed iterable (or other structure supporting __getitem__) that ...
torchtext/vocab.py
def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_): """ Set the vectors for the Vocab instance from a collection of Tensors. Arguments: stoi: A dictionary of string to the index of the associated vector in the `vectors` input argument. ...
def set_vectors(self, stoi, vectors, dim, unk_init=torch.Tensor.zero_): """ Set the vectors for the Vocab instance from a collection of Tensors. Arguments: stoi: A dictionary of string to the index of the associated vector in the `vectors` input argument. ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/vocab.py#L167-L189
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
SST.splits
Create dataset objects for splits of the SST dataset. Arguments: text_field: The field that will be used for the sentence. label_field: The field that will be used for label data. root: The root directory that the dataset's zip archive will be expanded into; ...
torchtext/datasets/sst.py
def splits(cls, text_field, label_field, root='.data', train='train.txt', validation='dev.txt', test='test.txt', train_subtrees=False, **kwargs): """Create dataset objects for splits of the SST dataset. Arguments: text_field: The field that will be used for the...
def splits(cls, text_field, label_field, root='.data', train='train.txt', validation='dev.txt', test='test.txt', train_subtrees=False, **kwargs): """Create dataset objects for splits of the SST dataset. Arguments: text_field: The field that will be used for the...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/sst.py#L47-L78
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
SST.iters
Create iterator objects for splits of the SST dataset. Arguments: batch_size: Batch_size device: Device to create batches on. Use - 1 for CPU and None for the currently active GPU device. root: The root directory that the dataset's zip archive will be ...
torchtext/datasets/sst.py
def iters(cls, batch_size=32, device=0, root='.data', vectors=None, **kwargs): """Create iterator objects for splits of the SST dataset. Arguments: batch_size: Batch_size device: Device to create batches on. Use - 1 for CPU and None for the currently active GPU d...
def iters(cls, batch_size=32, device=0, root='.data', vectors=None, **kwargs): """Create iterator objects for splits of the SST dataset. Arguments: batch_size: Batch_size device: Device to create batches on. Use - 1 for CPU and None for the currently active GPU d...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/sst.py#L81-L104
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
interleave_keys
Interleave bits from two sort keys to form a joint sort key. Examples that are similar in both of the provided keys will have similar values for the key defined by this function. Useful for tasks with two text fields like machine translation or natural language inference.
torchtext/data/utils.py
def interleave_keys(a, b): """Interleave bits from two sort keys to form a joint sort key. Examples that are similar in both of the provided keys will have similar values for the key defined by this function. Useful for tasks with two text fields like machine translation or natural language inference. ...
def interleave_keys(a, b): """Interleave bits from two sort keys to form a joint sort key. Examples that are similar in both of the provided keys will have similar values for the key defined by this function. Useful for tasks with two text fields like machine translation or natural language inference. ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/utils.py#L89-L98
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
RandomShuffler.use_internal_state
Use a specific RNG state.
torchtext/data/utils.py
def use_internal_state(self): """Use a specific RNG state.""" old_state = random.getstate() random.setstate(self._random_state) yield self._random_state = random.getstate() random.setstate(old_state)
def use_internal_state(self): """Use a specific RNG state.""" old_state = random.getstate() random.setstate(self._random_state) yield self._random_state = random.getstate() random.setstate(old_state)
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/utils.py#L127-L133
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
download_from_url
Download file, with logic (from tensor2tensor) for Google Drive
torchtext/utils.py
def download_from_url(url, path): """Download file, with logic (from tensor2tensor) for Google Drive""" def process_response(r): chunk_size = 16 * 1024 total_size = int(r.headers.get('Content-length', 0)) with open(path, "wb") as file: with tqdm(total=total_size, unit='B', ...
def download_from_url(url, path): """Download file, with logic (from tensor2tensor) for Google Drive""" def process_response(r): chunk_size = 16 * 1024 total_size = int(r.headers.get('Content-length', 0)) with open(path, "wb") as file: with tqdm(total=total_size, unit='B', ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/utils.py#L27-L57
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
unicode_csv_reader
Since the standard csv library does not handle unicode in Python 2, we need a wrapper. Borrowed and slightly modified from the Python docs: https://docs.python.org/2/library/csv.html#csv-examples
torchtext/utils.py
def unicode_csv_reader(unicode_csv_data, **kwargs): """Since the standard csv library does not handle unicode in Python 2, we need a wrapper. Borrowed and slightly modified from the Python docs: https://docs.python.org/2/library/csv.html#csv-examples""" if six.PY2: # csv.py doesn't do Unicode; e...
def unicode_csv_reader(unicode_csv_data, **kwargs): """Since the standard csv library does not handle unicode in Python 2, we need a wrapper. Borrowed and slightly modified from the Python docs: https://docs.python.org/2/library/csv.html#csv-examples""" if six.PY2: # csv.py doesn't do Unicode; e...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/utils.py#L60-L72
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
TranslationDataset.splits
Create dataset objects for splits of a TranslationDataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tuple containing the fields that will be used for data in each language. path (str): Common prefix of the splits'...
torchtext/datasets/translation.py
def splits(cls, exts, fields, path=None, root='.data', train='train', validation='val', test='test', **kwargs): """Create dataset objects for splits of a TranslationDataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tup...
def splits(cls, exts, fields, path=None, root='.data', train='train', validation='val', test='test', **kwargs): """Create dataset objects for splits of a TranslationDataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tup...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/translation.py#L45-L72
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Multi30k.splits
Create dataset objects for splits of the Multi30k dataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tuple containing the fields that will be used for data in each language. root: Root dataset storage directory. De...
torchtext/datasets/translation.py
def splits(cls, exts, fields, root='.data', train='train', validation='val', test='test2016', **kwargs): """Create dataset objects for splits of the Multi30k dataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tuple cont...
def splits(cls, exts, fields, root='.data', train='train', validation='val', test='test2016', **kwargs): """Create dataset objects for splits of the Multi30k dataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tuple cont...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/translation.py#L86-L114
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
IWSLT.splits
Create dataset objects for splits of the IWSLT dataset. Arguments: exts: A tuple containing the extension to path for each language. fields: A tuple containing the fields that will be used for data in each language. root: Root dataset storage directory. Defau...
torchtext/datasets/translation.py
def splits(cls, exts, fields, root='.data', train='train', validation='IWSLT16.TED.tst2013', test='IWSLT16.TED.tst2014', **kwargs): """Create dataset objects for splits of the IWSLT dataset. Arguments: exts: A tuple containing the extension to path for each lan...
def splits(cls, exts, fields, root='.data', train='train', validation='IWSLT16.TED.tst2013', test='IWSLT16.TED.tst2014', **kwargs): """Create dataset objects for splits of the IWSLT dataset. Arguments: exts: A tuple containing the extension to path for each lan...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/translation.py#L125-L161
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
RawField.process
Process a list of examples to create a batch. Postprocess the batch with user-provided Pipeline. Args: batch (list(object)): A list of object from a batch of examples. Returns: object: Processed object given the input and custom postprocessing Pipeline.
torchtext/data/field.py
def process(self, batch, *args, **kwargs): """ Process a list of examples to create a batch. Postprocess the batch with user-provided Pipeline. Args: batch (list(object)): A list of object from a batch of examples. Returns: object: Processed object given the inp...
def process(self, batch, *args, **kwargs): """ Process a list of examples to create a batch. Postprocess the batch with user-provided Pipeline. Args: batch (list(object)): A list of object from a batch of examples. Returns: object: Processed object given the inp...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L48-L61
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Field.preprocess
Load a single example using this field, tokenizing if necessary. If the input is a Python 2 `str`, it will be converted to Unicode first. If `sequential=True`, it will be tokenized. Then the input will be optionally lowercased and passed to the user-provided `preprocessing` Pipeline.
torchtext/data/field.py
def preprocess(self, x): """Load a single example using this field, tokenizing if necessary. If the input is a Python 2 `str`, it will be converted to Unicode first. If `sequential=True`, it will be tokenized. Then the input will be optionally lowercased and passed to the user-provided ...
def preprocess(self, x): """Load a single example using this field, tokenizing if necessary. If the input is a Python 2 `str`, it will be converted to Unicode first. If `sequential=True`, it will be tokenized. Then the input will be optionally lowercased and passed to the user-provided ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L204-L223
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Field.process
Process a list of examples to create a torch.Tensor. Pad, numericalize, and postprocess a batch and create a tensor. Args: batch (list(object)): A list of object from a batch of examples. Returns: torch.autograd.Variable: Processed object given the input and...
torchtext/data/field.py
def process(self, batch, device=None): """ Process a list of examples to create a torch.Tensor. Pad, numericalize, and postprocess a batch and create a tensor. Args: batch (list(object)): A list of object from a batch of examples. Returns: torch.autograd.Variabl...
def process(self, batch, device=None): """ Process a list of examples to create a torch.Tensor. Pad, numericalize, and postprocess a batch and create a tensor. Args: batch (list(object)): A list of object from a batch of examples. Returns: torch.autograd.Variabl...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L225-L238
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Field.pad
Pad a batch of examples using this field. Pads to self.fix_length if provided, otherwise pads to the length of the longest example in the batch. Prepends self.init_token and appends self.eos_token if those attributes are not None. Returns a tuple of the padded list and a list containing...
torchtext/data/field.py
def pad(self, minibatch): """Pad a batch of examples using this field. Pads to self.fix_length if provided, otherwise pads to the length of the longest example in the batch. Prepends self.init_token and appends self.eos_token if those attributes are not None. Returns a tuple of the ...
def pad(self, minibatch): """Pad a batch of examples using this field. Pads to self.fix_length if provided, otherwise pads to the length of the longest example in the batch. Prepends self.init_token and appends self.eos_token if those attributes are not None. Returns a tuple of the ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L240-L275
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Field.build_vocab
Construct the Vocab object for this field from one or more datasets. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that represents the set of possible values for this field. If ...
torchtext/data/field.py
def build_vocab(self, *args, **kwargs): """Construct the Vocab object for this field from one or more datasets. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that represents the set of...
def build_vocab(self, *args, **kwargs): """Construct the Vocab object for this field from one or more datasets. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that represents the set of...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L277-L309
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Field.numericalize
Turn a batch of examples that use this field into a Variable. If the field has include_lengths=True, a tensor of lengths will be included in the return value. Arguments: arr (List[List[str]], or tuple of (List[List[str]], List[int])): List of tokenized and padded ex...
torchtext/data/field.py
def numericalize(self, arr, device=None): """Turn a batch of examples that use this field into a Variable. If the field has include_lengths=True, a tensor of lengths will be included in the return value. Arguments: arr (List[List[str]], or tuple of (List[List[str]], List[in...
def numericalize(self, arr, device=None): """Turn a batch of examples that use this field into a Variable. If the field has include_lengths=True, a tensor of lengths will be included in the return value. Arguments: arr (List[List[str]], or tuple of (List[List[str]], List[in...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L311-L368
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
SubwordField.segment
Segment one or more datasets with this subword field. Arguments: Positional arguments: Dataset objects or other indexable mutable sequences to segment. If a Dataset object is provided, all columns corresponding to this field are used; individual colum...
torchtext/data/field.py
def segment(self, *args): """Segment one or more datasets with this subword field. Arguments: Positional arguments: Dataset objects or other indexable mutable sequences to segment. If a Dataset object is provided, all columns corresponding to this field are u...
def segment(self, *args): """Segment one or more datasets with this subword field. Arguments: Positional arguments: Dataset objects or other indexable mutable sequences to segment. If a Dataset object is provided, all columns corresponding to this field are u...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L424-L442
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NestedField.preprocess
Preprocess a single example. Firstly, tokenization and the supplied preprocessing pipeline is applied. Since this field is always sequential, the result is a list. Then, each element of the list is preprocessed using ``self.nesting_field.preprocess`` and the resulting list is returned. ...
torchtext/data/field.py
def preprocess(self, xs): """Preprocess a single example. Firstly, tokenization and the supplied preprocessing pipeline is applied. Since this field is always sequential, the result is a list. Then, each element of the list is preprocessed using ``self.nesting_field.preprocess`` and the...
def preprocess(self, xs): """Preprocess a single example. Firstly, tokenization and the supplied preprocessing pipeline is applied. Since this field is always sequential, the result is a list. Then, each element of the list is preprocessed using ``self.nesting_field.preprocess`` and the...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L528-L543
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NestedField.pad
Pad a batch of examples using this field. If ``self.nesting_field.sequential`` is ``False``, each example in the batch must be a list of string tokens, and pads them as if by a ``Field`` with ``sequential=True``. Otherwise, each example must be a list of list of tokens. Using ``self.nes...
torchtext/data/field.py
def pad(self, minibatch): """Pad a batch of examples using this field. If ``self.nesting_field.sequential`` is ``False``, each example in the batch must be a list of string tokens, and pads them as if by a ``Field`` with ``sequential=True``. Otherwise, each example must be a list of lis...
def pad(self, minibatch): """Pad a batch of examples using this field. If ``self.nesting_field.sequential`` is ``False``, each example in the batch must be a list of string tokens, and pads them as if by a ``Field`` with ``sequential=True``. Otherwise, each example must be a list of lis...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L545-L644
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NestedField.build_vocab
Construct the Vocab object for nesting field and combine it with this field's vocab. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that represents the set of possible values for the nesting fi...
torchtext/data/field.py
def build_vocab(self, *args, **kwargs): """Construct the Vocab object for nesting field and combine it with this field's vocab. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that repre...
def build_vocab(self, *args, **kwargs): """Construct the Vocab object for nesting field and combine it with this field's vocab. Arguments: Positional arguments: Dataset objects or other iterable data sources from which to construct the Vocab object that repre...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L646-L692
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NestedField.numericalize
Convert a padded minibatch into a variable tensor. Each item in the minibatch will be numericalized independently and the resulting tensors will be stacked at the first dimension. Arguments: arr (List[List[str]]): List of tokenized and padded examples. device (str or to...
torchtext/data/field.py
def numericalize(self, arrs, device=None): """Convert a padded minibatch into a variable tensor. Each item in the minibatch will be numericalized independently and the resulting tensors will be stacked at the first dimension. Arguments: arr (List[List[str]]): List of tokeni...
def numericalize(self, arrs, device=None): """Convert a padded minibatch into a variable tensor. Each item in the minibatch will be numericalized independently and the resulting tensors will be stacked at the first dimension. Arguments: arr (List[List[str]]): List of tokeni...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/field.py#L694-L723
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
batch
Yield elements from data in chunks of batch_size.
torchtext/data/iterator.py
def batch(data, batch_size, batch_size_fn=None): """Yield elements from data in chunks of batch_size.""" if batch_size_fn is None: def batch_size_fn(new, count, sofar): return count minibatch, size_so_far = [], 0 for ex in data: minibatch.append(ex) size_so_far = batc...
def batch(data, batch_size, batch_size_fn=None): """Yield elements from data in chunks of batch_size.""" if batch_size_fn is None: def batch_size_fn(new, count, sofar): return count minibatch, size_so_far = [], 0 for ex in data: minibatch.append(ex) size_so_far = batc...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/iterator.py#L255-L271
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
pool
Sort within buckets, then batch, then shuffle batches. Partitions data into chunks of size 100*batch_size, sorts examples within each chunk using sort_key, then batch these examples and shuffle the batches.
torchtext/data/iterator.py
def pool(data, batch_size, key, batch_size_fn=lambda new, count, sofar: count, random_shuffler=None, shuffle=False, sort_within_batch=False): """Sort within buckets, then batch, then shuffle batches. Partitions data into chunks of size 100*batch_size, sorts examples within each chunk using sort_ke...
def pool(data, batch_size, key, batch_size_fn=lambda new, count, sofar: count, random_shuffler=None, shuffle=False, sort_within_batch=False): """Sort within buckets, then batch, then shuffle batches. Partitions data into chunks of size 100*batch_size, sorts examples within each chunk using sort_ke...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/iterator.py#L274-L293
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Iterator.splits
Create Iterator objects for multiple splits of a dataset. Arguments: datasets: Tuple of Dataset objects corresponding to the splits. The first such object should be the train set. batch_sizes: Tuple of batch sizes to use for the different splits, or None ...
torchtext/data/iterator.py
def splits(cls, datasets, batch_sizes=None, **kwargs): """Create Iterator objects for multiple splits of a dataset. Arguments: datasets: Tuple of Dataset objects corresponding to the splits. The first such object should be the train set. batch_sizes: Tuple of bat...
def splits(cls, datasets, batch_sizes=None, **kwargs): """Create Iterator objects for multiple splits of a dataset. Arguments: datasets: Tuple of Dataset objects corresponding to the splits. The first such object should be the train set. batch_sizes: Tuple of bat...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/iterator.py#L79-L97
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Iterator.data
Return the examples in the dataset in order, sorted, or shuffled.
torchtext/data/iterator.py
def data(self): """Return the examples in the dataset in order, sorted, or shuffled.""" if self.sort: xs = sorted(self.dataset, key=self.sort_key) elif self.shuffle: xs = [self.dataset[i] for i in self.random_shuffler(range(len(self.dataset)))] else: x...
def data(self): """Return the examples in the dataset in order, sorted, or shuffled.""" if self.sort: xs = sorted(self.dataset, key=self.sort_key) elif self.shuffle: xs = [self.dataset[i] for i in self.random_shuffler(range(len(self.dataset)))] else: x...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/iterator.py#L99-L107
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Iterator.init_epoch
Set up the batch generator for a new epoch.
torchtext/data/iterator.py
def init_epoch(self): """Set up the batch generator for a new epoch.""" if self._restored_from_state: self.random_shuffler.random_state = self._random_state_this_epoch else: self._random_state_this_epoch = self.random_shuffler.random_state self.create_batches() ...
def init_epoch(self): """Set up the batch generator for a new epoch.""" if self._restored_from_state: self.random_shuffler.random_state = self._random_state_this_epoch else: self._random_state_this_epoch = self.random_shuffler.random_state self.create_batches() ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/iterator.py#L109-L125
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Batch.fromvars
Create a Batch directly from a number of Variables.
torchtext/data/batch.py
def fromvars(cls, dataset, batch_size, train=None, **kwargs): """Create a Batch directly from a number of Variables.""" batch = cls() batch.batch_size = batch_size batch.dataset = dataset batch.fields = dataset.fields.keys() for k, v in kwargs.items(): setattr...
def fromvars(cls, dataset, batch_size, train=None, **kwargs): """Create a Batch directly from a number of Variables.""" batch = cls() batch.batch_size = batch_size batch.dataset = dataset batch.fields = dataset.fields.keys() for k, v in kwargs.items(): setattr...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/batch.py#L37-L45
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
UDPOS.splits
Downloads and loads the Universal Dependencies Version 2 POS Tagged data.
torchtext/datasets/sequence_tagging.py
def splits(cls, fields, root=".data", train="en-ud-tag.v2.train.txt", validation="en-ud-tag.v2.dev.txt", test="en-ud-tag.v2.test.txt", **kwargs): """Downloads and loads the Universal Dependencies Version 2 POS Tagged data. """ return super(UDPOS, cls).split...
def splits(cls, fields, root=".data", train="en-ud-tag.v2.train.txt", validation="en-ud-tag.v2.dev.txt", test="en-ud-tag.v2.test.txt", **kwargs): """Downloads and loads the Universal Dependencies Version 2 POS Tagged data. """ return super(UDPOS, cls).split...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/sequence_tagging.py#L57-L66
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
CoNLL2000Chunking.splits
Downloads and loads the CoNLL 2000 Chunking dataset. NOTE: There is only a train and test dataset so we use 10% of the train set as validation
torchtext/datasets/sequence_tagging.py
def splits(cls, fields, root=".data", train="train.txt", test="test.txt", validation_frac=0.1, **kwargs): """Downloads and loads the CoNLL 2000 Chunking dataset. NOTE: There is only a train and test dataset so we use 10% of the train set as validation """ tr...
def splits(cls, fields, root=".data", train="train.txt", test="test.txt", validation_frac=0.1, **kwargs): """Downloads and loads the CoNLL 2000 Chunking dataset. NOTE: There is only a train and test dataset so we use 10% of the train set as validation """ tr...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/sequence_tagging.py#L78-L103
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
IMDB.splits
Create dataset objects for splits of the IMDB dataset. Arguments: text_field: The field that will be used for the sentence. label_field: The field that will be used for label data. root: Root dataset storage directory. Default is '.data'. train: The directory tha...
torchtext/datasets/imdb.py
def splits(cls, text_field, label_field, root='.data', train='train', test='test', **kwargs): """Create dataset objects for splits of the IMDB dataset. Arguments: text_field: The field that will be used for the sentence. label_field: The field that will be used fo...
def splits(cls, text_field, label_field, root='.data', train='train', test='test', **kwargs): """Create dataset objects for splits of the IMDB dataset. Arguments: text_field: The field that will be used for the sentence. label_field: The field that will be used fo...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/imdb.py#L40-L55
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
WikiText2.splits
Create dataset objects for splits of the WikiText-2 dataset. This is the most flexible way to use the dataset. Arguments: text_field: The field that will be used for text data. root: The root directory that the dataset's zip archive will be expanded into; theref...
torchtext/datasets/language_modeling.py
def splits(cls, text_field, root='.data', train='wiki.train.tokens', validation='wiki.valid.tokens', test='wiki.test.tokens', **kwargs): """Create dataset objects for splits of the WikiText-2 dataset. This is the most flexible way to use the dataset. Arguments: ...
def splits(cls, text_field, root='.data', train='wiki.train.tokens', validation='wiki.valid.tokens', test='wiki.test.tokens', **kwargs): """Create dataset objects for splits of the WikiText-2 dataset. This is the most flexible way to use the dataset. Arguments: ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/language_modeling.py#L40-L60
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
WikiText2.iters
Create iterator objects for splits of the WikiText-2 dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. Arguments: batch_size: Batch size. bptt_len: Length of sequences for backpropagation th...
torchtext/datasets/language_modeling.py
def iters(cls, batch_size=32, bptt_len=35, device=0, root='.data', vectors=None, **kwargs): """Create iterator objects for splits of the WikiText-2 dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. ...
def iters(cls, batch_size=32, bptt_len=35, device=0, root='.data', vectors=None, **kwargs): """Create iterator objects for splits of the WikiText-2 dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/language_modeling.py#L63-L91
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Pipeline.call
Apply _only_ the convert_token function of the current pipeline to the input. If the input is a list, a list with the results of applying the `convert_token` function to all input elements is returned. Arguments: x: The input to apply the convert_token function to. ...
torchtext/data/pipeline.py
def call(self, x, *args): """Apply _only_ the convert_token function of the current pipeline to the input. If the input is a list, a list with the results of applying the `convert_token` function to all input elements is returned. Arguments: x: The input to apply the...
def call(self, x, *args): """Apply _only_ the convert_token function of the current pipeline to the input. If the input is a list, a list with the results of applying the `convert_token` function to all input elements is returned. Arguments: x: The input to apply the...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/pipeline.py#L40-L53
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Pipeline.add_before
Add a Pipeline to be applied before this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply before this Pipeline.
torchtext/data/pipeline.py
def add_before(self, pipeline): """Add a Pipeline to be applied before this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply before this Pipeline. """ if not isinstance(pipeline, Pipeline): pipeline = Pipeline(pipeline) ...
def add_before(self, pipeline): """Add a Pipeline to be applied before this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply before this Pipeline. """ if not isinstance(pipeline, Pipeline): pipeline = Pipeline(pipeline) ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/pipeline.py#L55-L65
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Pipeline.add_after
Add a Pipeline to be applied after this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply after this Pipeline.
torchtext/data/pipeline.py
def add_after(self, pipeline): """Add a Pipeline to be applied after this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply after this Pipeline. """ if not isinstance(pipeline, Pipeline): pipeline = Pipeline(pipeline) ...
def add_after(self, pipeline): """Add a Pipeline to be applied after this processing pipeline. Arguments: pipeline: The Pipeline or callable to apply after this Pipeline. """ if not isinstance(pipeline, Pipeline): pipeline = Pipeline(pipeline) ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/pipeline.py#L67-L77
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
check_split_ratio
Check that the split ratio argument is not malformed
torchtext/data/dataset.py
def check_split_ratio(split_ratio): """Check that the split ratio argument is not malformed""" valid_ratio = 0. if isinstance(split_ratio, float): # Only the train set relative ratio is provided # Assert in bounds, validation size is zero assert 0. < split_ratio < 1., ( "...
def check_split_ratio(split_ratio): """Check that the split ratio argument is not malformed""" valid_ratio = 0. if isinstance(split_ratio, float): # Only the train set relative ratio is provided # Assert in bounds, validation size is zero assert 0. < split_ratio < 1., ( "...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/dataset.py#L284-L311
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Dataset.split
Create train-test(-valid?) splits from the instance's examples. Arguments: split_ratio (float or List of floats): a number [0, 1] denoting the amount of data to be used for the training split (rest is used for validation), or a list of numbers denoting the relative s...
torchtext/data/dataset.py
def split(self, split_ratio=0.7, stratified=False, strata_field='label', random_state=None): """Create train-test(-valid?) splits from the instance's examples. Arguments: split_ratio (float or List of floats): a number [0, 1] denoting the amount of data to be u...
def split(self, split_ratio=0.7, stratified=False, strata_field='label', random_state=None): """Create train-test(-valid?) splits from the instance's examples. Arguments: split_ratio (float or List of floats): a number [0, 1] denoting the amount of data to be u...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/dataset.py#L86-L136
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Dataset.download
Download and unzip an online archive (.zip, .gz, or .tgz). Arguments: root (str): Folder to download data to. check (str or None): Folder whose existence indicates that the dataset has already been downloaded, or None to check the existence of root/{cls.n...
torchtext/data/dataset.py
def download(cls, root, check=None): """Download and unzip an online archive (.zip, .gz, or .tgz). Arguments: root (str): Folder to download data to. check (str or None): Folder whose existence indicates that the dataset has already been downloaded, or ...
def download(cls, root, check=None): """Download and unzip an online archive (.zip, .gz, or .tgz). Arguments: root (str): Folder to download data to. check (str or None): Folder whose existence indicates that the dataset has already been downloaded, or ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/dataset.py#L157-L199
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
Dataset.filter_examples
Remove unknown words from dataset examples with respect to given field. Arguments: field_names (list(str)): Within example only the parts with field names in field_names will have their unknown words deleted.
torchtext/data/dataset.py
def filter_examples(self, field_names): """Remove unknown words from dataset examples with respect to given field. Arguments: field_names (list(str)): Within example only the parts with field names in field_names will have their unknown words deleted. """ for...
def filter_examples(self, field_names): """Remove unknown words from dataset examples with respect to given field. Arguments: field_names (list(str)): Within example only the parts with field names in field_names will have their unknown words deleted. """ for...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/data/dataset.py#L201-L214
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NLIDataset.splits
Create dataset objects for splits of the SNLI dataset. This is the most flexible way to use the dataset. Arguments: text_field: The field that will be used for premise and hypothesis data. label_field: The field that will be used for label data. pars...
torchtext/datasets/nli.py
def splits(cls, text_field, label_field, parse_field=None, extra_fields={}, root='.data', train='train.jsonl', validation='val.jsonl', test='test.jsonl'): """Create dataset objects for splits of the SNLI dataset. This is the most flexible way to use the dataset. A...
def splits(cls, text_field, label_field, parse_field=None, extra_fields={}, root='.data', train='train.jsonl', validation='val.jsonl', test='test.jsonl'): """Create dataset objects for splits of the SNLI dataset. This is the most flexible way to use the dataset. A...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/nli.py#L46-L88
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
NLIDataset.iters
Create iterator objects for splits of the SNLI dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. Arguments: batch_size: Batch size. device: Device to create batches on. Use -1 for CPU and No...
torchtext/datasets/nli.py
def iters(cls, batch_size=32, device=0, root='.data', vectors=None, trees=False, **kwargs): """Create iterator objects for splits of the SNLI dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. ...
def iters(cls, batch_size=32, device=0, root='.data', vectors=None, trees=False, **kwargs): """Create iterator objects for splits of the SNLI dataset. This is the simplest way to use the dataset, and assumes common defaults for field, vocabulary, and iterator parameters. ...
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pytorch/text
python
https://github.com/pytorch/text/blob/26bfce6869dc704f1d86792f9a681d453d7e7bb8/torchtext/datasets/nli.py#L91-L126
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26bfce6869dc704f1d86792f9a681d453d7e7bb8
train
bytes_to_unicode
Decode given bytes using specified escaping method. :param byte_data: The byte-like object with bytes to decode. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ASCII' bytes. Defaults to False. :return: New unicode string, escaped with ...
rdbtools/encodehelpers.py
def bytes_to_unicode(byte_data, escape, skip_printable=False): """ Decode given bytes using specified escaping method. :param byte_data: The byte-like object with bytes to decode. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ASCI...
def bytes_to_unicode(byte_data, escape, skip_printable=False): """ Decode given bytes using specified escaping method. :param byte_data: The byte-like object with bytes to decode. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ASCI...
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sripathikrishnan/redis-rdb-tools
python
https://github.com/sripathikrishnan/redis-rdb-tools/blob/543a73e84702e911ddcd31325ecfde77d7fd230b/rdbtools/encodehelpers.py#L96-L123
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543a73e84702e911ddcd31325ecfde77d7fd230b
train
apply_escape_bytes
Apply the specified escape method on the given bytes. :param byte_data: The byte-like object with bytes to escape. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ASCII' bytes. Defaults to False. :return: new bytes object with the escap...
rdbtools/encodehelpers.py
def apply_escape_bytes(byte_data, escape, skip_printable=False): """ Apply the specified escape method on the given bytes. :param byte_data: The byte-like object with bytes to escape. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ...
def apply_escape_bytes(byte_data, escape, skip_printable=False): """ Apply the specified escape method on the given bytes. :param byte_data: The byte-like object with bytes to escape. :param escape: The escape method to use. :param skip_printable: If True, don't escape byte_data with all 'printable ...
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sripathikrishnan/redis-rdb-tools
python
https://github.com/sripathikrishnan/redis-rdb-tools/blob/543a73e84702e911ddcd31325ecfde77d7fd230b/rdbtools/encodehelpers.py#L126-L154
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543a73e84702e911ddcd31325ecfde77d7fd230b
train
RdbParser._decode_module_id
decode module id to string based on @antirez moduleTypeNameByID function from redis/src/module.c :param module_id: 64bit integer :return: string
rdbtools/parser.py
def _decode_module_id(self, module_id): """ decode module id to string based on @antirez moduleTypeNameByID function from redis/src/module.c :param module_id: 64bit integer :return: string """ name = [''] * 9 module_id >>= 10 for i in reversed(rang...
def _decode_module_id(self, module_id): """ decode module id to string based on @antirez moduleTypeNameByID function from redis/src/module.c :param module_id: 64bit integer :return: string """ name = [''] * 9 module_id >>= 10 for i in reversed(rang...
[ "decode", "module", "id", "to", "string", "based", "on" ]
sripathikrishnan/redis-rdb-tools
python
https://github.com/sripathikrishnan/redis-rdb-tools/blob/543a73e84702e911ddcd31325ecfde77d7fd230b/rdbtools/parser.py#L941-L953
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543a73e84702e911ddcd31325ecfde77d7fd230b
train
format_step
Return the step value in format suitable for display.
hiddenlayer/history.py
def format_step(step, zero_prefix=False): """Return the step value in format suitable for display.""" if isinstance(step, int): return "{:06}".format(step) if zero_prefix else "{}".format(step) elif isinstance(step, tuple): return "{:04}:{:06}".format(*step) if zero_prefix else "{}:{}".forma...
def format_step(step, zero_prefix=False): """Return the step value in format suitable for display.""" if isinstance(step, int): return "{:06}".format(step) if zero_prefix else "{}".format(step) elif isinstance(step, tuple): return "{:04}:{:06}".format(*step) if zero_prefix else "{}:{}".forma...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/history.py#L27-L32
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
History.log
Record metrics at a specific step. E.g. my_history.log(34, loss=2.3, accuracy=0.2) Okay to call multiple times for the same step. New values overwrite older ones if they have the same metric name. step: An integer or tuple of integers. If a tuple, then the first value is c...
hiddenlayer/history.py
def log(self, step, **kwargs): """Record metrics at a specific step. E.g. my_history.log(34, loss=2.3, accuracy=0.2) Okay to call multiple times for the same step. New values overwrite older ones if they have the same metric name. step: An integer or tuple of integers. If a tu...
def log(self, step, **kwargs): """Record metrics at a specific step. E.g. my_history.log(34, loss=2.3, accuracy=0.2) Okay to call multiple times for the same step. New values overwrite older ones if they have the same metric name. step: An integer or tuple of integers. If a tu...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/history.py#L67-L88
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
History.get_total_time
Returns the total period between when the first and last steps where logged. This usually correspnods to the total training time if there were no gaps in the training.
hiddenlayer/history.py
def get_total_time(self): """Returns the total period between when the first and last steps where logged. This usually correspnods to the total training time if there were no gaps in the training. """ first_step = self.steps[0] last_step = self.steps[-1] seconds =...
def get_total_time(self): """Returns the total period between when the first and last steps where logged. This usually correspnods to the total training time if there were no gaps in the training. """ first_step = self.steps[0] last_step = self.steps[-1] seconds =...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/history.py#L125-L134
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.outgoing
Returns nodes connecting out of the given node (or list of nodes).
hiddenlayer/graph.py
def outgoing(self, node): """Returns nodes connecting out of the given node (or list of nodes).""" nodes = node if isinstance(node, list) else [node] node_ids = [self.id(n) for n in nodes] # Find edges outgoing from this group but not incoming to it outgoing = [self[e[1]] for e i...
def outgoing(self, node): """Returns nodes connecting out of the given node (or list of nodes).""" nodes = node if isinstance(node, list) else [node] node_ids = [self.id(n) for n in nodes] # Find edges outgoing from this group but not incoming to it outgoing = [self[e[1]] for e i...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L221-L228
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.siblings
Returns all nodes that share the same parent (incoming node) with the given node, including the node itself.
hiddenlayer/graph.py
def siblings(self, node): """Returns all nodes that share the same parent (incoming node) with the given node, including the node itself. """ incoming = self.incoming(node) # TODO: Not handling the case of multiple incoming nodes yet if len(incoming) == 1: inc...
def siblings(self, node): """Returns all nodes that share the same parent (incoming node) with the given node, including the node itself. """ incoming = self.incoming(node) # TODO: Not handling the case of multiple incoming nodes yet if len(incoming) == 1: inc...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L239-L250
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.remove
Remove a node and its edges.
hiddenlayer/graph.py
def remove(self, nodes): """Remove a node and its edges.""" nodes = nodes if isinstance(nodes, list) else [nodes] for node in nodes: k = self.id(node) self.edges = list(filter(lambda e: e[0] != k and e[1] != k, self.edges)) del self.nodes[k]
def remove(self, nodes): """Remove a node and its edges.""" nodes = nodes if isinstance(nodes, list) else [nodes] for node in nodes: k = self.id(node) self.edges = list(filter(lambda e: e[0] != k and e[1] != k, self.edges)) del self.nodes[k]
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L258-L264
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.replace
Replace nodes with node. Edges incoming to nodes[0] are connected to the new node, and nodes outgoing from nodes[-1] become outgoing from the new node.
hiddenlayer/graph.py
def replace(self, nodes, node): """Replace nodes with node. Edges incoming to nodes[0] are connected to the new node, and nodes outgoing from nodes[-1] become outgoing from the new node.""" nodes = nodes if isinstance(nodes, list) else [nodes] # Is the new node part of the replac...
def replace(self, nodes, node): """Replace nodes with node. Edges incoming to nodes[0] are connected to the new node, and nodes outgoing from nodes[-1] become outgoing from the new node.""" nodes = nodes if isinstance(nodes, list) else [nodes] # Is the new node part of the replac...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L266-L286
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.search
Searches the graph for a sub-graph that matches the given pattern and returns the first match it finds.
hiddenlayer/graph.py
def search(self, pattern): """Searches the graph for a sub-graph that matches the given pattern and returns the first match it finds. """ for node in self.nodes.values(): match, following = pattern.match(self, node) if match: return match, followin...
def search(self, pattern): """Searches the graph for a sub-graph that matches the given pattern and returns the first match it finds. """ for node in self.nodes.values(): match, following = pattern.match(self, node) if match: return match, followin...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L288-L296
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.sequence_id
Make up an ID for a sequence (list) of nodes. Note: `getrandbits()` is very uninformative as a "readable" ID. Here, we build a name such that when the mouse hovers over the drawn node in Jupyter, one can figure out which original nodes make up the sequence. This is actually quite useful.
hiddenlayer/graph.py
def sequence_id(self, sequence): """Make up an ID for a sequence (list) of nodes. Note: `getrandbits()` is very uninformative as a "readable" ID. Here, we build a name such that when the mouse hovers over the drawn node in Jupyter, one can figure out which original nodes make up the sequ...
def sequence_id(self, sequence): """Make up an ID for a sequence (list) of nodes. Note: `getrandbits()` is very uninformative as a "readable" ID. Here, we build a name such that when the mouse hovers over the drawn node in Jupyter, one can figure out which original nodes make up the sequ...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L299-L309
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
Graph.build_dot
Generate a GraphViz Dot graph. Returns a GraphViz Digraph object.
hiddenlayer/graph.py
def build_dot(self): """Generate a GraphViz Dot graph. Returns a GraphViz Digraph object. """ from graphviz import Digraph # Build GraphViz Digraph dot = Digraph() dot.attr("graph", bgcolor=self.theme["background_color"], color...
def build_dot(self): """Generate a GraphViz Dot graph. Returns a GraphViz Digraph object. """ from graphviz import Digraph # Build GraphViz Digraph dot = Digraph() dot.attr("graph", bgcolor=self.theme["background_color"], color...
[ "Generate", "a", "GraphViz", "Dot", "graph", "." ]
waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/graph.py#L311-L355
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294f8732b271cbdd6310c55bdf5ce855cbf61c75
train
to_data
Standardize data types. Converts PyTorch tensors to Numpy arrays, and Numpy scalars to Python scalars.
hiddenlayer/utils.py
def to_data(value): """Standardize data types. Converts PyTorch tensors to Numpy arrays, and Numpy scalars to Python scalars.""" # TODO: Use get_framework() for better detection. if value.__class__.__module__.startswith("torch"): import torch if isinstance(value, torch.nn.parameter.Param...
def to_data(value): """Standardize data types. Converts PyTorch tensors to Numpy arrays, and Numpy scalars to Python scalars.""" # TODO: Use get_framework() for better detection. if value.__class__.__module__.startswith("torch"): import torch if isinstance(value, torch.nn.parameter.Param...
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waleedka/hiddenlayer
python
https://github.com/waleedka/hiddenlayer/blob/294f8732b271cbdd6310c55bdf5ce855cbf61c75/hiddenlayer/utils.py#L17-L35
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294f8732b271cbdd6310c55bdf5ce855cbf61c75