import time

import socketio



sio = socketio.Client()

start_timer = None





def send_ping():

    global start_timer

    start_timer = time.time()

    sio.emit('ping_from_client')





@sio.event

def connect():

    print('connected to server')

    send_ping()





@sio.event

def pong_from_server(data):

    global start_timer

    latency = time.time() - start_timer

    print('latency is {0:.2f} ms'.format(latency * 1000))

    sio.sleep(1)

    send_ping()





if __name__ == '__main__':

    sio.connect('http://localhost:5000')

    sio.wait()

    print('next')

import jieba.posseg as pseg

seg_list = pseg.cut("我爱北京天安门")

for word,flag in seg_list:

    print(word)

    print(flag)

---------------------------------------------------------------------------

TypeError                                 Traceback (most recent call last)

<ipython-input-5-f105f6980f88> in <module>()

      1 import jieba.posseg as pseg

      2 seg_list = pseg.cut("我爱北京天安门")

----> 3 for word,flag in seg_list:

      4     print(word)

      5     print(flag)



TypeError: cannot unpack non-iterable pair object

原因是新版本中seg_list是一个生成器，所以只能 for win seg_list

然后从word中解包出来

print(w.word)

print(w.flag)

这样问题就解决了。

# -*-coding=utf-8-*-



# @Time : 2019/9/9 9:25

# @File : mail_backup.py

# @Author : 

import codecs

import re

import config

import os

from exchangelib import DELEGATE, Account, Credentials, Configuration, NTLM, Message, Mailbox, HTMLBody,FileAttachment,ItemAttachment

from exchangelib.protocol import BaseProtocol, NoVerifyHTTPAdapter





#此句用来消除ssl证书错误，exchange使用自签证书需加上

BaseProtocol.HTTP_ADAPTER_CLS = NoVerifyHTTPAdapter





# 输入你的域账号如example\xxx

cred = Credentials(r'example\xxx', 你的邮箱密码)



configx = Configuration(server='mail.credlink.com', credentials=cred, auth_type=NTLM)

a = Account(

    primary_smtp_address='你的邮箱地址', config=configx, autodiscover=False, access_type=DELEGATE

)





for item in a.inbox.all().order_by('-datetime_received')[:100]:

    print(item.subject, item.sender, item.unique_body,item.datetime_received)

   

    name = item.subject

    name = re.sub('[\/:*?"<>|]', '-', name)

    local_path = os.path.join('inbox', name+'.html')

    with codecs.open(local_path, 'w','utf-8') as f:

        f.write(item.unique_body)



    for attachment in item.attachments:

        if isinstance(attachment, FileAttachment):

            name = attachment.name

            name = re.sub('[\/:*?"<>|]','-',name)

            local_path = os.path.join('inbox', attachment.name)

            with codecs.open(local_path, 'wb') as f:

                f.write(attachment.content)

            print('Saved attachment to', local_path)



        elif isinstance(attachment, ItemAttachment):

            if isinstance(attachment.item, Message):

                name=attachment.item.subject

                name = re.sub('[\/:*?"<>|]', '-', name)

                local_path = os.path.join('inbox', 'attachment')

                with codecs.open(local_path, 'w') as f:

                    f.write(attachment.item.body)

import time



LOOP = 2*10**8



def add(x,y):

    return x+y



def cpu_pressure(loop):

    

    for i in range(loop):

        result = add(i,i+1)





if __name__ == '__main__':

    start = time.time()

    cpu_pressure(LOOP)

    print(f'time used {time.time()-start}s')

    from . import (constants, error, message, context,

ImportError: DLL load failed: 找不到指定的模块。

from random import randint



randint(0,1)

Out[25]: 1



randint(0,1)

Out[26]: 1



randint(0,1)

Out[27]: 1



randint(0,1)

Out[28]: 1



randint(0,1)

Out[29]: 0



randint(0,1)

Out[30]: 1

	credentials = pika.PlainCredentials('admin','admin')

	connection = pika.BlockingConnection(pika.ConnectionParameters('192.168.1.101',5672,'/',credentials))



	channel = connection.channel()



	channel.exchange_declare(exchange='logs',exchange_type='fanout')

	from scrapy.selector import Selector

	

	def get_response_callback(content):

		txt = str(content,encoding='utf-8')

		resp = Selector(text=txt)

		title = resp.xpath('//title/text()').extract_first()

		print(title)

	

	@defer.inlineCallbacks

	def task():

		url = 'http://www.baidu.com'

		d=getPage(url.encode('utf-8'))

		d.addCallback(get_response_callback)

		yield d



	def done():

		reactor.stop()



	def done1(*args,**kwargs):

		reactor.stop()



	task_list = 

	for i in range(4):

		d=task()

		task_list.append(d)



	dd = defer.DeferredList(task_list)



	dd.addBoth(done)



	reactor.run()

distanceTransform

Calculates the distance to the closest zero pixel for each pixel of the source image.





Python: cv2.distanceTransform(src, distanceType, maskSize[, dst]) → dst



Python: cv.DistTransform(src, dst, distance_type=CV_DIST_L2, mask_size=3, mask=None, labels=None) → None

Parameters:	

src – 8-bit, single-channel (binary) source image.

dst – Output image with calculated distances. It is a 32-bit floating-point, single-channel image of the same size as src .

distanceType – Type of distance. It can be CV_DIST_L1, CV_DIST_L2 , or CV_DIST_C .

maskSize – Size of the distance transform mask. It can be 3, 5, or CV_DIST_MASK_PRECISE (the latter option is only supported by the first function). In case of the CV_DIST_L1 or CV_DIST_C distance type, the parameter is forced to 3 because a  3\times 3 mask gives the same result as  5\times 5 or any larger aperture.

labels – Optional output 2D array of labels (the discrete Voronoi diagram). It has the type CV_32SC1 and the same size as src . See the details below.

labelType – Type of the label array to build. If labelType==DIST_LABEL_CCOMP then each connected component of zeros in src (as well as all the non-zero pixels closest to the connected component) will be assigned the same label. If labelType==DIST_LABEL_PIXEL then each zero pixel (and all the non-zero pixels closest to it) gets its own label.

The functions distanceTransform calculate the approximate or precise distance from every binary image pixel to the nearest zero pixel. For zero image pixels, the distance will obviously be zero.

When maskSize == CV_DIST_MASK_PRECISE and distanceType == CV_DIST_L2 , the function runs the algorithm described in [Felzenszwalb04]. This algorithm is parallelized with the TBB library.



In other cases, the algorithm [Borgefors86] is used. This means that for a pixel the function finds the shortest path to the nearest zero pixel consisting of basic shifts: horizontal, vertical, diagonal, or knight’s move (the latest is available for a 5\times 5 mask). The overall distance is calculated as a sum of these basic distances. Since the distance function should be symmetric, all of the horizontal and vertical shifts must have the same cost (denoted as a ), all the diagonal shifts must have the same cost (denoted as b ), and all knight’s moves must have the same cost (denoted as c ). For the CV_DIST_C and CV_DIST_L1 types, the distance is calculated precisely, whereas for CV_DIST_L2 (Euclidean distance) the distance can be calculated only with a relative error (a 5\times 5 mask gives more accurate results). For a,``b`` , and c , OpenCV uses the values suggested in the original paper:



CV_DIST_C	(3\times 3)	a = 1, b = 1

CV_DIST_L1	(3\times 3)	a = 1, b = 2

CV_DIST_L2	(3\times 3)	a=0.955, b=1.3693

CV_DIST_L2	(5\times 5)	a=1, b=1.4, c=2.1969

Typically, for a fast, coarse distance estimation CV_DIST_L2, a 3\times 3 mask is used. For a more accurate distance estimation CV_DIST_L2 , a 5\times 5 mask or the precise algorithm is used. Note that both the precise and the approximate algorithms are linear on the number of pixels.



The second variant of the function does not only compute the minimum distance for each pixel (x, y) but also identifies the nearest connected component consisting of zero pixels (labelType==DIST_LABEL_CCOMP) or the nearest zero pixel (labelType==DIST_LABEL_PIXEL). Index of the component/pixel is stored in \texttt{labels}(x, y) . When labelType==DIST_LABEL_CCOMP, the function automatically finds connected components of zero pixels in the input image and marks them with distinct labels. When labelType==DIST_LABEL_CCOMP, the function scans through the input image and marks all the zero pixels with distinct labels.



In this mode, the complexity is still linear. That is, the function provides a very fast way to compute the Voronoi diagram for a binary image. Currently, the second variant can use only the approximate distance transform algorithm, i.e. maskSize=CV_DIST_MASK_PRECISE is not supported yet.



Note

An example on using the distance transform can be found at opencv_source_code/samples/cpp/distrans.cpp

(Python) An example on using the distance transform can be found at opencv_source/samples/python2/distrans.py