我有一个非常简单的图表,我想绘制为svg.例如:
# graph.dot
graph {
a -- b;
b -- c;
}
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我目前正在使用pydot读取文件,然后生成svg文件,如下所示:
import pydot
graphs = pydot.graph_from_dot_file('graph.dot')
graphs[0].write_svg('graph.svg') # there is only 1 graph so the 0 index.
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但是,我需要这样做而不需要中间文件graph.dot
和graph.svg
.我有graph.dot
一个字符串中的代码内容,对应于我需要字符串中的svg输出.
我需要这样的东西:
graph_dot = "... ..." # string, I have this
graph_svg = convert_dot_to_svg(graph_dot)
# i need something like convert_dot_to_svg()
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我的问题不仅限于pydot.如果有人知道我可以做到这一点的web api,那么它也会这样做.
非常感谢,提前.
我想spaCy
使用我提供的句子分割边界而不是它自己的处理。
例如:
get_sentences("Bob meets Alice. @SentBoundary@ They play together.")
# => ["Bob meets Alice.", "They play together."] # two sents
get_sentences("Bob meets Alice. They play together.")
# => ["Bob meets Alice. They play together."] # ONE sent
get_sentences("Bob meets Alice, @SentBoundary@ they play together.")
# => ["Bob meets Alice,", "they play together."] # two sents
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这是我到目前为止所拥有的(从此处的文档中借用东西):
import spacy
nlp = spacy.load('en_core_web_sm')
def mark_sentence_boundaries(doc):
for i, token in enumerate(doc):
if token.text == '@SentBoundary@':
doc[i+1].sent_start = True
return …
Run Code Online (Sandbox Code Playgroud) 是否有惯用的python方式列出列表中特定大小的所有组合?
下面的代码在ruby(这里)中工作,我想知道是否有python等效于此:
a = [1, 2, 3, 4]
a.combination(1).to_a #=> [[1],[2],[3],[4]]
a.combination(2).to_a #=> [[1,2],[1,3],[1,4],[2,3],[2,4],[3,4]]
a.combination(3).to_a #=> [[1,2,3],[1,2,4],[1,3,4],[2,3,4]]
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PS:我不是在寻找排列,而是寻找特定大小的组合.
非常感谢 : )
splitDescription = work.workdescription.downcase.split(' ')
keywords = ['teamwork', 'developed', 'enhanced', 'transformed', 'achieved','grew', 'introduced', 'project', 'awarded',
'planned','supervised','created','designed','discovered','evaluated','promoted','represented','completed',
'devised','liaised','controlled','researched','organised','achieved','managed','analysed','assessed','conducted',
'solved','responsible for','responsibilities']
splitDescription.select {|word| word.include?(keywords)}.each do |word|
new_score += 0.5
end
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我有一个splitDescription
拆分并存储描述.
我想看看是否有任何关键字都在splitDescription
和每个keyword
在splitDescription
的new_score
0.5上升.
我正在尝试此代码,但它无法正常工作.