Question What are the differences between a Framework build and a non-Framework build (ie, standard UNIX build) of Python on Mac OS X? Also, what are the advantages and disadvantages of each? Preliminary Research Here is the information that I found prior to posting this question: [Pythonmac-SIG] Why is Framework build of Python needed B. Grainger: "I seem to recall that a Framewo
题 在Mac OS X上，Framework构建与Python的非构建（即标准UNIX构建）Python之间有什么区别？ 另外，每个的优点和缺点是什么？ 初步研究 以下是发布此问题之前发现的信息： [Pythonmac-SIG]为什么需要Python的Framework构建 B. Grainger：“我似乎记得，如果你想用本地Mac GUI做任何事情，就需要Python的Framework构建，我的理解是否正确？” C. Barker：“非常多 - 要访问Mac GUI，应用程序需要安装在适当的Mac应用程
I have some code that does an awful lot of string formatting, Often, I end up with code along the lines of: "...".format(x=x, y=y, z=z, foo=foo, ...) Where I'm trying to interpolate a large number of variables into a large string. Is there a good reason not to write a function like this that uses the inspect module to find variables to interpolate? import inspect def interpolate(s):
我有一些代码可以完成很多字符串格式化工作，通常我会按照以下代码结束代码： "...".format(x=x, y=y, z=z, foo=foo, ...) 我试图将大量变量插入到大字符串中。 是否有一个很好的理由不写这样的函数，它使用inspect模块来查找要插入的变量？ import inspect def interpolate(s): return s.format(**inspect.currentframe().f_back.f_locals) def generateTheString(x): y = foo(x) z = x + y # more calcu
I have been studying Python, and I read a chapter which describes the None value, but unfortunately this book isn't very clear at some points. I thought that I would find the answer to my question, if I share it there. I want to know what the None value is and what do you use it for? And also, I don't get this part of the book: Assigning a value of None to a variable is one way to
我一直在研究Python，并且阅读了描述None值的一章，但不幸的是，这本书在某些方面并不十分清晰。 我想我会找到我的问题的答案，如果我在那里分享。 我想知道“ None值是什么以及你使用它的目的是什么？ 而且，我没有得到这本书的这一部分： 分配的值None给一个变量是将其重置到其原始的，空的状态的一种方法。 那是什么意思？ 答案很棒，但由于我对计算机世界的知识水平低下（我还没有学过类，对象等），所以我并不了
I have PyCharm 3.0 installed for Windows and installed IronPython 2.7.4 installed. But it appears that i am not able to get references and it wont recognize .net classes to some degree. Let me give you a simple example: import clr clr.AddReference("System.Windows.Forms") from System.Windows.Forms import MessageBox MessageBox.Show("Hello World") i can run/execute it perfectly fine but the ID
我为Windows安装了PyCharm 3.0，并安装了IronPython 2.7.4。 但似乎我无法获得引用，并且它在某种程度上不会识别.net类。 让我举个简单的例子： import clr clr.AddReference("System.Windows.Forms") from System.Windows.Forms import MessageBox MessageBox.Show("Hello World") 我可以很好地运行/执行它，但IDE向我显示它无法识别System 正如你可以想象的，这是有点......令人沮丧地把它温和地按下alt + enter然后为
I am new to machine learning and wondering the difference between kmeans and kmeans2 in scipy. According to the doc both of them are using the 'k-means' algorithm, but how to choose them? Based on the documentation, it seems kmeans2 is the standard k-means algorithm and runs until converging to a local optimum - and allows you to change the seed initialization. The kmeans function wi
我是机器学习的新手，想知道komeans和kmeans2在scipy中的区别。 根据文档他们都使用'k-means'算法，但如何选择它们？ 根据文档，似乎kmeans2是标准的k-means算法，并运行到收敛到局部最优 - 并允许您更改种子初始化。 kmeans函数将基于缺少变化而提前终止，因此甚至可能无法达到局部最优。 此外，它的目标是生成一个码本来映射特征向量。 码本本身不一定是从停止点生成的，而是使用具有最低“失真”的迭代来生成码
Given the following code: from django.db import transaction @transaction.atomic def viewfunc(request): # This code executes inside a transaction. do_stuff() From my understanding of transactions in Django 1.6 if do_stuff throws an exception, say an IntegrityError, then the transaction will be rolled back right. But since Django itself is calling the view nothing will stop the Integrit
给出以下代码： from django.db import transaction @transaction.atomic def viewfunc(request): # This code executes inside a transaction. do_stuff() 根据我对Django 1.6中的事务的理解，如果do_stuff抛出一个异常，比如一个IntegrityError，那么这个事务将被回滚。 但是由于Django自己正在调用视图，没有任何东西会阻止IntegrityError从调用栈上升并导致HTTP 500错误是吗？ 让我们假设这不是我们想要的，因
I am trying to parse multiple pcap files using the pynids library, but can get to parse only the 1st file. I saw that there was a function nids_unregister_tcp in libnids, will that help? I can't find that function in pynids though. import nids def handle_tcp_stream(tcp): print "In handle_tcp_stream" def extract(pcap_file): nids.param("tcp_workarounds", 1) nids.param("pcap_f
我正在尝试使用pynids库解析多个pcap文件，但只能解析第一个文件。 我看到在libnids中有一个函数nids_unregister_tcp ，会有帮助吗？ 尽管如此，我无法在pynids中找到该功能。 import nids def handle_tcp_stream(tcp): print "In handle_tcp_stream" def extract(pcap_file): nids.param("tcp_workarounds", 1) nids.param("pcap_filter", "tcp") # bpf restrict to TCP only, note nids.param("
I wrote a function that gets as an input a list of unique ints in order,(from small to big). Im supposed to find in the list an index that matches the value in the index. for example if L[2]==2 the output is true. so after i did that in complexity O(logn) i now want to find how many indexes behave like that in the given list with the same complexity O(logn). im uploading my first code that do
我写了一个函数，按照顺序（从小到大）将唯一整数列表作为输入。 我应该在列表中找到与索引中的值匹配的索引。 例如，如果L [2] = 2，则输出为真。 所以在我做了复杂的O（logn）之后，我现在想要查找具有相同复杂度O（logn）的给定列表中有多少个索引的行为。 即时通讯上传我的第一个代码，做第一部分和第二个代码，我需要帮助： def steady_state(L): lower= 0 upper= len(L) -1 while lower<=upper:
I am starting celery via supervisord, see the entry below. [program:celery] user = foobar autostart = true autorestart = true directory = /opt/src/slicephone/cloud command = /opt/virtenvs/django_slice/bin/celery beat --app=cloud -l DEBUG -s /home/foobar/run/celerybeat-schedule --pidfile=/home/foobar/run/celerybeat.pid priority = 100 stdout_logfile_backups = 0 stderr_logfile_backups = 0 stdout_lo
我通过supervisord启动芹菜，请参阅下面的条目。 [program:celery] user = foobar autostart = true autorestart = true directory = /opt/src/slicephone/cloud command = /opt/virtenvs/django_slice/bin/celery beat --app=cloud -l DEBUG -s /home/foobar/run/celerybeat-schedule --pidfile=/home/foobar/run/celerybeat.pid priority = 100 stdout_logfile_backups = 0 stderr_logfile_backups = 0 stdout_logfile_maxbyte
I need to execute a pool of many parallel database connections and queries. I would like to use a multiprocessing.Pool or concurrent.futures ProcessPoolExecutor. Python 2.7.5 In some cases, query requests take too long or will never finish (hung/zombie process). I would like to kill the specific process from the multiprocessing.Pool or concurrent.futures ProcessPoolExecutor that has timed ou
我需要执行许多并行数据库连接和查询池。 我想使用一个multiprocessing.Pool或concurrent.futures ProcessPoolExecutor。 Python 2.7.5 在某些情况下，查询请求会花费太长时间或永远不会结束（挂起/僵尸进程）。 我想杀死已经超时的multiprocessing.Pool或concurrent.futures ProcessPoolExecutor的具体过程。 下面是如何杀死/重新生成整个进程池的示例，但理想情况下，我会尽量减少CPU抖动，因为我只想杀死一个特定的长