Windows下利用win32clipboard实现Python的剪切板(Clipboard)操作

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import win32clipboard as wc
import win32con
import sys

def stripClipboard():
    # 开始剪切板操作
    wc.OpenClipboard()
    # 尝试将剪切板内容读取为Unicode文本
    # 如果复制的内容是文件而非文本，读取为Unicode文本会报错，所以需要错误处理。
    try:
        txt = wc.GetClipboardData(win32con.CF_UNICODETEXT)
    except Exception as e:
        print("剪切板内容非文本，无法去除换行符。")
        wc.EmptyClipboard()
        sys.exit("已清空剪切板并退出。")
    txt = wc.GetClipboardData(win32con.CF_UNICODETEXT)
    txt = str(txt).strip()
    # 字符串按行分割
    txt = txt.splitlines()
    n = len(txt)
    # 用空格拼接每行
    txt = ' '.join(txt)
    # 将所有长度大于1的空白符转为1个空格
    txt = ' '.join(txt.split())
    # 清空剪切板
    wc.EmptyClipboard()
    # 尝试将处理完的字符放入剪切板，注意这里用的是win32con.CF_UNICODETEXT，
    # 如果使用win32con.CF_TEXT则需要对txt进行编码，否则会出现乱码。
    wc.SetClipboardData(win32con.CF_UNICODETEXT, txt)
    # 关闭剪切板
    wc.CloseClipboard()
    print('删除了{}个换行符n'.format(n-1))
    print(txt+'n')

if __name__ == '__main__':
    stripClipboard()

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In this paper, we propose a novel neural
network model called RNN Encoder–
Decoder that consists of two recurrent
neural networks (RNN). One RNN encodes
a sequence of symbols into a fixedlength
vector representation, and the other
decodes the representation into another sequence
of symbols. The encoder and decoder
of the proposed model are jointly
trained to maximize the conditional probability
of a target sequence given a source
sequence. The performance of a statistical
machine translation system is empirically
found to improve by using the conditional
probabilities of phrase pairs computed
by the RNN Encoder–Decoder as an
additional feature in the existing log-linear
model. Qualitatively, we show that the
proposed model learns a semantically and
syntactically meaningful representation of
linguistic phrases.

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In this paper, we propose a novel neural network model called RNN Encoder– Decoder that consists of two recurrent neural networks (RNN). One RNN encodes a sequence of symbols into a fixedlength vector representation, and the other decodes the representation into another sequence of symbols. The encoder and decoder of the proposed model are jointly trained to maximize the conditional probability of a target sequence given a source sequence. The performance of a statistical machine translation system is empirically found to improve by using the conditional probabilities of phrase pairs computed by the RNN Encoder–Decoder as an additional feature in the existing log-linear model. Qualitatively, we show that the proposed model learns a semantically and syntactically meaningful representation of linguistic phrases.