How to make two markers share the same label in the legend using matplotlib?

What I want is like this: What I get is this: So how to merge the markers into one label? also for the lines, for the lines, of course, u can realize it by not assigning label to the second line while using the same linetype, but for the markers, you can not, since they are of different shapes. I think it's best to use a full legend - otherwise, how will your readers know the differenc

如何使两个标记使用matplotlib在图例中共享相同的标签?

我想要的是这样的: 我得到的是这样的: 那么如何将标记合并为一个标签呢? 对于线条来说,对于线条,当然,你可以通过在使用相同线型时不将标签分配给第二行来实现,但对于标记,由于它们具有不同的形状,所以不能。 我认为最好使用完整的图例 - 否则,您的读者将如何知道两个模型或两个数据集之间的差异? 我会这样做: 但是,如果您真的想按照自己的方式进行操作,则可以使用本指南中显示的自定义图例。 您需要像

Removing every other element in NumPy

I can't for the life of me figure this out. I'm trying to remove every other element in the second axis of an array. I did this in MATLAB with arr(:,:,2:2:end) = []; , but when I tried to do the same in Python and compare the two outputs, I get a different matrix. I've tried arr = np.delete(arr,np.arange(0,arr.shape[2],2),2) and arr = arr[:,:,1::2] , but neither seem to come up

删除NumPy中的所有其他元素

我不能为我的生活弄清楚这一点。 我试图删除数组的第二个轴上的其他元素。 我在MATLAB中用arr(:,:,2:2:end) = []; ,但是当我试图在Python中做同样的事情并比较两个输出时,我得到了不同的矩阵。 我试过arr = np.delete(arr,np.arange(0,arr.shape[2],2),2)和arr = arr[:,:,1::2] ,但似乎都没有出现用MATLAB获得的东西。 例: MATLAB disp(['before: ',str(arr[21,32,11])]) arr(:,:,2:2:end) = []; disp

Integer square root in python

Is there an integer square root somewhere in python, or in standard libraries? I want it to be exact (ie return an integer), and bark if there's no solution. At the moment I rolled my own naive one: def isqrt(n): i = int(math.sqrt(n) + 0.5) if i**2 == n: return i raise ValueError('input was not a perfect square') But it's ugly and I don't really trust it for l

在python中的整数平方根

python中或标准库中是否有整数平方根? 我希望它是确切的(即返回一个整数),如果没有解决方案,就会吠叫。 目前我推出了自己的天真版: def isqrt(n): i = int(math.sqrt(n) + 0.5) if i**2 == n: return i raise ValueError('input was not a perfect square') 但它很丑,我不太相信大整数。 如果我超过了这个值,我可以遍历这些方块并放弃,但我认为这样做会有点慢。 另外我想我可能会重新发明轮

How to calc square root in python?

Why does python give the "wrong" answer? x= 16 sqrt= x**(.5) returns 4 sqrt= x**(1/2) returns 1 Yes, I know import math and use sqrt . But I'm looking for an answer to the above. sqrt=x**(1/2) is doing integer division. 1/2 == 0 . So you're computing x(1/2) in the first instance, x(0) in the second. So it's not wrong, it's the right answer to a different ques

如何计算python的平方根?

为什么python会给出“错误”的答案? x= 16 sqrt= x**(.5) returns 4 sqrt= x**(1/2) returns 1 是的,我知道import math并使用sqrt 。 但我正在寻找上述答案。 sqrt=x**(1/2)正在做整数除法。 1/2 == 0 。 所以你首先计算x(1/2),第二次计算x(0)。 所以这没有错,这是另一个问题的正确答案。 您必须编写: sqrt = x**(1/2.0) ,否则执行整数除法并且表达式1/2返回0 。 这种行为在Python 2.x中是“正常的”,而

How to implement an efficient infinite generator of prime numbers in Python?

This is not a homework, I am just curious. INFINITE is the key word here. I wish to use it as for p in primes(). I believe that this is a built-in function in Haskell. So, the answer cannot be as naive as "Just do a Sieve". First of all, you do not know how many consecutive primes will be consumed. Well, suppose you could concoct 100 of them at a time. Would you use the same

如何在Python中实现一个有效的素数数字生成器?

这不是一项家庭作业,我只是好奇。 INFINITE是这里的关键词。 我希望将它用作primes()中的p。 我相信这是Haskell中的一个内置函数。 所以,答案不能像“只是做一个筛子”那样幼稚。 首先,你不知道会消耗多少连续的素数。 那么,假设你可以一次制造100个。 你会使用相同的Sieve方法以及素数公式的频率吗? 我更喜欢非并发方法。 感谢您阅读(和写作))! 我仍然喜欢我在这里写的东西(与其他许多作者合作的食

Fastest way to list all primes below N

This is the best algorithm I could come up. def get_primes(n): numbers = set(range(n, 1, -1)) primes = [] while numbers: p = numbers.pop() primes.append(p) numbers.difference_update(set(range(p*2, n+1, p))) return primes >>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import get_primes').timeit(1) 1.1499958793645562 Can it be ma

最快的方式列出N以下的所有素数

这是我能想到的最好的算法。 def get_primes(n): numbers = set(range(n, 1, -1)) primes = [] while numbers: p = numbers.pop() primes.append(p) numbers.difference_update(set(range(p*2, n+1, p))) return primes >>> timeit.Timer(stmt='get_primes.get_primes(1000000)', setup='import get_primes').timeit(1) 1.1499958793645562 它可以做得更快吗? 这个代

How could I check if a number is a perfect square?

How could I check if a number is a perfect square? Speed is of no concern, for now, just working. The problem with relying on any floating point computation ( math.sqrt(x) , or x**0.5 ) is that you can't really be sure it's exact (for sufficiently large integers x , it won't be, and might even overflow). Fortunately (if one's in no hurry;-) there are many pure integer approac

我怎么能检查一个数字是一个完美的广场?

我怎么能检查一个数字是一个完美的广场? 现在,速度不用担心,只是在工作。 依赖任何浮点运算( math.sqrt(x)或x**0.5 )的问题是,你不能确定它是否确切(对于足够大的整数x ,它不会是,甚至可能是溢出)。 幸运的是(如果一个人不急,;-)有许多纯整数方法,比如下面的......: def is_square(apositiveint): x = apositiveint // 2 seen = set([x]) while x * x != apositiveint: x = (x + (apositiveint //

Asynchronous RabbitMQ consumer with aioamqp

I'm trying to write an asynchronous consumer using asyncio/aioamqp. My problem is, the callback coroutine (below) is blocking. I set the channel to do a basic_consume(), and assign the callback as callback(). The callback has a "yield from asyncio.sleep" statement (to simulate "work"), which takes an integer from the publisher and sleeps for that amount of time before p

带有aioamqp的异步RabbitMQ消费者

我正在尝试使用asyncio / aioamqp编写异步使用者。 我的问题是,回调协程(下面)阻塞。 我将通道设置为执行basic_consume(),并将回调分配为callback()。 该回调具有“从asyncio.sleep产生”语句(模拟“工作”),该语句从发布者处获取一个整数,并在打印该消息之前休眠该段时间。 如果我发布了两条消息,一条的时间为“10”,紧接着是一条的时间为“1”,我预计第二条消息将首先打印,因为它具有较短的睡眠时间。 相反,回

Why single python process's cpu usage can be more than 100 percent?

Because of GIL, I thought a multi-thread python process can only have one thread running at one time, thus the cpu usage can not be more than 100 percent. But I found the code bellow can occupy 950% cpu usage in top. import threading import time def f(): while 1: pass for i in range(10): t = threading.Thread(target=f) t.setDaemon(True) t.start() time.sleep(60) This

为什么单个Python进程的CPU使用率可能超过100%?

由于GIL,我认为多线程python进程一次只能运行一个线程,因此cpu的使用率不能超过100%。 但是我发现这个代码可以占用顶部950%的cpu使用量。 import threading import time def f(): while 1: pass for i in range(10): t = threading.Thread(target=f) t.setDaemon(True) t.start() time.sleep(60) 这不是Python解释器使用多达130%CPU的问题。 这怎么可能? 在那个问题中,OP说他正在进行I

python + wsgi on a multi

Suppose that I've written a wsgi application . I run this application on Apache2 on Linux with multi-threaded mod-wsgi configuration, so that my application is run in many threads per single process: WSGIDaemonProcess mysite processes=3 threads=2 display-name=mod_wsgi WSGIProcessGroup mysite WSGIScriptAlias / /some/path/wsgi.py The application code is: def application(environ, start_respo

python + wsgi上多

假设我写了一个wsgi application 。 我在Linux使用多线程mod-wsgi配置在Apache2上运行此应用程序,以便我的应用程序在每个进程的多个线程中运行: WSGIDaemonProcess mysite processes=3 threads=2 display-name=mod_wsgi WSGIProcessGroup mysite WSGIScriptAlias / /some/path/wsgi.py 应用程序代码是: def application(environ, start_response): from foo import racer status = '200 OK' response_headers