I want to download an affymetrix annotation file. But it needs to log in first. The log in page is https://www.affymetrix.com/estore/user/login.jsp The file I want to download is: http://www.affymetrix.com/Auth/analysis/downloads/na32/genotyping/GenomeWideSNP_6.na32.annot.db.zip I have try some method but I cannot figure it out. # from requests import session payload = {
我想下载一个affymetrix注释文件。 但它需要先登录。 登录页面是https://www.affymetrix.com/estore/user/login.jsp我要下载的文件是：http://www.affymetrix.com/Auth/analysis/downloads/na32/genotyping/ GenomeWideSNP_6.na32.annot.db.zip 我尝试了一些方法，但我无法弄清楚。 ＃ from requests import session payload = { 'action': 'login', 'username': 'username', #This part sho
I want to generate graph form an adjacency list but I am not happy with how the nodes are positioned. I want them to be positioned according to a pre-defined scheme which resembles a regular grid with arbitrary coordinates x and y, while still maintaining the scale-free features. Let me give an example: A barabasi-albert network with node 1 located at x_1 = 0.6 and y_1 = 0.5, node 2 located at
我想生成一个邻接表的图形，但我不满意节点的位置。 我希望它们按照预定义的方案进行定位，该方案类似于具有任意坐标x和y的规则网格，同时仍保持无标度特征。 举个例子：一个节点1的barabasi-albert网络位于x_1 = 0.6和y_1 = 0.5，节点2位于x_2 = -0.5和y_2 = 1 ...等等。 我有一个每个节点的坐标列表。 在这里查看draw_networkx_XXX函数的pos参数。 它可以像这样使用： import networkx as nx import matplotlib.pyplo
I'm interested in network analysis on large networks with millions of nodes and tens of millions of edges. I want to be able to do things like parse networks from many formats, find connected components, detect communities, and run centrality measures like PageRank. I am attracted to NetworkX because it has a nice api, good documentation, and has been under active development for years. P
我对具有数百万个节点和数千万个边缘的大型网络的网络分析感兴趣。 我希望能够做许多事情，比如解析来自多种格式的网络，查找连接的组件，检测社区，以及运行像PageRank这样的中心度量。 我被NetworkX吸引，因为它有一个很好的api，很好的文档，并且多年来一直在积极开发中。 另外，因为它是在python中，它应该很快与开发。 在最近的演示文稿中（这里的幻灯片可在github上获得），它声称： 与许多其他工具不同，NX被设计
I am using gitpython library for performing git operations, retrieve git info from python code. I want to retrieve all revisions for a specific file. But couldn't find a specific reference for this on the docs. Can anybody give some clue on which function will help in this regard? Thanks. There is no such function, but it is easily implemented: import git repo = git.Repo() path = "dir
我使用gitpython库来执行git操作，从python代码中检索git信息。 我想检索特定文件的所有修订。 但是在文档中找不到具体的参考文献。 任何人都可以提供一些线索，说明哪些功能在这方面会有所帮助？ 谢谢。 没有这样的功能，但很容易实现： import git repo = git.Repo() path = "dir/file_you_are_looking_for" commits_touching_path = list(repo.iter_commits(paths=path)) 即使涉及多条路径，性能也会适中。 基准和
What's the difference? What are the advantages / disadvantages of tuples / lists? Apart from tuples being immutable there is also a semantic distinction that should guide their usage. Tuples are heterogeneous data structures (ie, their entries have different meanings), while lists are homogeneous sequences. Tuples have structure, lists have order. Using this distinction makes code mor
有什么不同？ 元组/列表有什么优点/缺点？ 除了元组是不可变的，还有一个应该引导它们用法的语义区别。 元组是异构数据结构（即它们的条目具有不同的含义），而列表是同类序列。 元组有结构，列表有顺序。 使用这种区别使代码更加明确和易于理解。 一个例子是成对的页面和行号来引用书中的位置，例如： my_location = (42, 11) # page number, line number 然后，您可以将其用作字典中的关键字来存储位置的注释。
I am writing a Spark application and want to combine a set of Key-Value pairs (K, V1), (K, V2), ..., (K, Vn) into one Key-Multivalue pair (K, [V1, V2, ..., Vn]) . I feel like I should be able to do this using the reduceByKey function with something of the flavor: My_KMV = My_KV.reduce(lambda a, b: a.append([b])) The error that I get when this occurs is: 'NoneType' object has no attri
我正在写一个Spark应用程序，并希望将一组键值对(K, V1), (K, V2), ..., (K, Vn)成一个键 - 多值对(K, [V1, V2, ..., Vn]) 。 我觉得我应该可以使用reduceByKey函数来实现这一点： My_KMV = My_KV.reduce(lambda a, b: a.append([b])) 发生这种情况时出现的错误是： 'NoneType'对象没有'append'属性。 我的键是整数，值V1，...，Vn是元组。 我的目标是用键和值列表（元组）创建一个单对。 Map和Reduce
This question already has an answer here: Difference between append vs. extend list methods in Python 22 answers use extend not append ; append appends a single element whereas extend appends all elemnets of a given list
这个问题在这里已经有了答案： Python中的append和extend列表方法之间的区别22回答 使用extend不能append ; append追加单个元素，而extend追加给定列表的所有元素
This question already has an answer here: Difference between append vs. extend list methods in Python 22 answers Writing a list to a file with Python 15 answers
这个问题在这里已经有了答案： Python中的append和extend列表方法之间的区别22回答 用Python 15写一个列表到一个文件的答案
This question already has an answer here: Difference between append vs. extend list methods in Python 22 answers 使用append： l1 = [] t1 = (1.0 , (2.0,3.0)) l1.append((t1)) t2 = (4.0 , (5.0,6.0)) l1.append(t2) print(l1) l2 = [(1.0, (2.0,3.0)), (4.0, (5.0,6.0))] print(l2) Changing it to append() does the trick. l1 = [] t1 = (1.0 , (2.0,3.0)) l1.append((t1)) t2 = (4.0 , (5.0,6.0)) l1.ap
这个问题在这里已经有了答案： Python中的append和extend列表方法之间的区别22回答 使用append： l1 = [] t1 = (1.0 , (2.0,3.0)) l1.append((t1)) t2 = (4.0 , (5.0,6.0)) l1.append(t2) print(l1) l2 = [(1.0, (2.0,3.0)), (4.0, (5.0,6.0))] print(l2) 将它更改为append（）会有诀窍。 l1 = [] t1 = (1.0 , (2.0,3.0)) l1.append((t1)) t2 = (4.0 , (5.0,6.0)) l1.append(t2) print(l1) l2 = [(1.0, (2.0,3.0)),
This question already has an answer here: Difference between append vs. extend list methods in Python 22 answers 尝试这个： child.extend(parent[:index])
这个问题在这里已经有了答案： Python中的append和extend列表方法之间的区别22回答 尝试这个： child.extend(parent[:index])