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150 lines
2.5 KiB
Markdown
150 lines
2.5 KiB
Markdown
---
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title: liteyuki.comm.channel_
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order: 1
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icon: laptop-code
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category: API
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---
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### ***def*** `set_channel(name: str, channel: Channel) -> None`
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设置通道实例
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Args:
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name: 通道名称
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channel: 通道实例
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### ***def*** `set_channels(channels: dict[str, Channel]) -> None`
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设置通道实例
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Args:
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channels: 通道名称
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### ***def*** `get_channel(name: str) -> Channel`
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获取通道实例
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Args:
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name: 通道名称
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Returns:
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### ***def*** `get_channels() -> dict[str, Channel]`
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获取通道实例
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Returns:
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### ***def*** `on_set_channel(data: tuple[str, dict[str, Any]]) -> None`
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### ***def*** `on_get_channel(data: tuple[str, dict[str, Any]]) -> None`
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### ***def*** `on_get_channels(data: tuple[str, dict[str, Any]]) -> None`
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### ***def*** `decorator(func: Callable[[T], Any]) -> Callable[[T], Any]`
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### ***async def*** `wrapper(data: T) -> Any`
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### ***class*** `Channel(Generic[T])`
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通道类,可以在进程间和进程内通信,双向但同时只能有一个发送者和一个接收者
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有两种接收工作方式,但是只能选择一种,主动接收和被动接收,主动接收使用 `receive` 方法,被动接收使用 `on_receive` 装饰器
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###   ***def*** `__init__(self, _id: str, type_check: bool) -> None`
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 初始化通道
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Args:
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_id: 通道ID
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###   ***def*** `send(self, data: T) -> None`
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 发送数据
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Args:
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data: 数据
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###   ***def*** `receive(self) -> T`
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 接收数据
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Args:
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###   ***def*** `close(self) -> None`
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 关闭通道
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###   ***def*** `on_receive(self, filter_func: Optional[FILTER_FUNC]) -> Callable[[Callable[[T], Any]], Callable[[T], Any]]`
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 接收数据并执行函数
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Args:
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filter_func: 过滤函数,为None则不过滤
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Returns:
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装饰器,装饰一个函数在接收到数据后执行
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### ***var*** `T = TypeVar('T')`
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### ***var*** `channel_deliver_active_channel = Channel(_id='channel_deliver_active_channel')`
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### ***var*** `channel_deliver_passive_channel = Channel(_id='channel_deliver_passive_channel')`
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### ***var*** `recv_chan = data[1]['recv_chan']`
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### ***var*** `recv_chan = Channel[Channel[Any]]('recv_chan')`
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### ***var*** `recv_chan = Channel[dict[str, Channel[Any]]]('recv_chan')`
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### ***var*** `data = self.conn_recv.recv()`
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### ***var*** `func = _callback_funcs[func_id]`
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### ***var*** `func = _callback_funcs[func_id]`
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### ***var*** `data = self.conn_recv.recv()`
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### ***var*** `data = self.conn_recv.recv()`
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