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

最近翻译论文的时候发现复制过来的文字经常带有很多的换行符，为了方便的去除这些换行符，写了一个python小方法。
代码如下(适用于Python3)：

import win32clipboard as wcimport win32condef stripClipboard():    wc.OpenClipboard()    txt = wc.GetClipboardData(win32con.CF_UNICODETEXT)    n=0    txt=str(txt).strip()    for x in ["\r\n","\n","\r"]:        n=n+txt.count(x)        txt=txt.replace(x,'')    wc.EmptyClipboard()    wc.SetClipboardData(win32con.CF_UNICODETEXT, txt.encode())    wc.CloseClipboard()    print('删除了'+str(n)+'个换行符\n')    print(txt+'\n')stripClipboard()

保存并命名为“stripClipboard.py”。每当从论文里复制了文字后，只需运行python stripClipboard.py接着再ctrl+v就能粘贴已去除所有换行符的文字内容。

例如，从论文中ctrl+v了如下文字：

In this paper, we propose a novel neuralnetwork model called RNN Encoder–Decoder that consists of two recurrentneural networks (RNN). One RNN encodesa sequence of symbols into a fixedlengthvector representation, and the otherdecodes the representation into another sequenceof symbols. The encoder and decoderof the proposed model are jointlytrained to maximize the conditional probabilityof a target sequence given a sourcesequence. The performance of a statisticalmachine translation system is empiricallyfound to improve by using the conditionalprobabilities of phrase pairs computedby the RNN Encoder–Decoder as anadditional feature in the existing log-linearmodel. Qualitatively, we show that theproposed model learns a semantically andsyntactically meaningful representation oflinguistic phrases.

运行python stripClipboard.py，则剪切板的内容变为：

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.

注1： 要使用win32clipboard需安装pypiwin32，可用pip install pypiwin32来进行安装
注2： Mac下Python的剪切板操作将在另一篇文章里介绍
注3(已解决)：处理中文字符时会乱码