Python-сообщество

 from aiohttp import web
import asyncio
async def send_msg1(app):
	while True:
		for ws in app['admin_sockets']:
			await ws.send_str('msg1')
		await asyncio.sleep(10)
async def send_msg2(app):
	while True:
		for ws in app['admin_sockets']:
			await ws.send_str('msg2')
		await asyncio.sleep(20)
async def ws_handler(request):
	ws = web.WebSocketResponse()
	await ws.prepare(request)
	is_admin = request.path.startswith('/ws/admin')
	a_socks = request.app['admin_sockets']
	u_socks = request.app['user_sockets']
	if is_admin:
		a_socks.append(ws)
	else:
		u_socks.append(ws)	
	try:
		async for msg in ws:
			await ws.send_str(msg.data)
	except Exception as e:
		pass
	finally:		
		if is_admin:
			a_socks.remove(ws)
		else:
			u_socks.remove(ws)
	return ws
app = web.Application()
app.router.add_get('/ws', ws_handler)
app.router.add_get('/ws/admin', ws_handler)
app['admin_sockets'] = []
app['user_sockets'] = []
loop = asyncio.get_event_loop()
loop.create_task(send_msg1(app))
loop.create_task(send_msg2(app))

 myFile = open("input.txt", "r", encoding="utf8")
k = int(myFile.readline())
myList = []
for line in myFile:
    newLine = line.split()
    if int(newLine[-1]) >= 40 and int(newLine[-2]) >= 40 \
            and int(newLine[-3]) >= 40:
        myList.append(newLine)
myFile.close()
myList.sort(key=lambda a: int(a[-1]) + int(a[-2]) + int(a[-3]))
myList.reverse()
konk = []
for i in myList:
    sum = int(i[-1]) + int(i[-2]) + int(i[-3])
    konk.append(sum)
n = len(konk)
 
 
def konkurs(n, k, konk):
    if n <= k:
        return 0
    elif konk[k] == konk[0]:
        return 1
    for i in range(k, 0, -1):
        if konk[i] < konk[i - 1]:
            return konk[i - 1]
 
 
print(konkurs(n, k, konk))

 fileInput = open('input.txt', 'r', encoding='utf8')
scoreList = []
k = int(fileInput.readline())
for line in fileInput:
    i = line.split()
    if int(i[-1]) >= 40 and int(i[-2]) >= 40 and int(i[-3]) >= 40:
        scoreList.append(int(i[-1]) + int(i[-2]) + int(i[-3]))
fileInput.close()
lenScoreList = len(scoreList)
scoreList.sort()
 
 
def minScore(n, k, sL):
    if n <= k:
        return 0
    elif sL[-k] == sL[n + 1]:
        return 1
    for i in range(-k, -1):
        if sL[i] < sL[i + 1]:
            return sL[i + 1]
 
 
print(minScore(lenScoreList, k, scoreList))

 systemctl status gunicorn

 [Unit]                                                                                                                                                      
  2 Description=gunicorn daemon
  3 After=network.target
  4  
  5 [Service]
  6 User=root
  7 Group=www-data
  8 WorkingDirectory=/root/projects/chat
  9 ExecStart=/root/projects/chat/chat_env/bin/gunicorn --workers 3 --bind unix:/root/projects/chat/chat.sock chat.wsgi:application
 10  
 11 [Install]
 12 WantedBy=multi-user.target
 13  

 Jan 24 21:16:02 localhost gunicorn[4856]:   File "/root/projects/chat/chat_env/lib/python3.5/site-packages/gunicorn/util.py", line 352, in import_app
Jan 24 21:16:02 localhost gunicorn[4856]:     __import__(module)
Jan 24 21:16:02 localhost gunicorn[4856]: ImportError: No module named 'chat.wsgi'

 tree -L 3

 ├── chat
  3 │   ├── chat
  4 │   │   ├── __init__.py
  5 │   │   ├── __pycache__
  6 │   │   ├── settings.py
  7 │   │   ├── urls.py
  8 │   │   └── wsgi.py
  9 │   ├── manage.py
 10 │   └── static
 11 │       └── admin
 12 ├── chat_env
 13 │   ├── bin
 14 │   │   ├── activate
 15 │   │   ├── activate.csh
 16 │   │   ├── activate.fish
 17 │   │   ├── activate_this.py
 18 │   │   ├── django-admin
 19 │   │   ├── django-admin.py
 20 │   │   ├── easy_install
 21 │   │   ├── easy_install-3.5
 22 │   │   ├── gunicorn
 23 │   │   ├── gunicorn_paster
 24 │   │   ├── pip
 25 │   │   ├── pip3
 26 │   │   ├── pip3.5
 27 │   │   ├── __pycache__
 28 │   │   ├── python -> python3
 29 │   │   ├── python3
 30 │   │   ├── python3.5 -> python3
 31 │   │   ├── python-config
 32 │   │   └── wheel
 33 │   ├── include
 34 │   │   └── python3.5m -> /usr/include/python3.5m
 35 │   ├── lib
 36 │   │   └── python3.5
 37 │   └── pip-selfcheck.json

 import os 
import imaplib
import email
import base64
import email.message
import base64
imaplib.IMAP4.debug = imaplib.IMAP4_SSL.debug = 1
con = imaplib.IMAP4_SSL('imap.mail.ru',993)
con.login('pythonsutest@mail.ru','ktozdes90')
con.select()
typ, data = con.search(None, 'ALL')
print(data)
for num in data[0].split():
    typ, data = con.fetch(num, '(RFC822)')
    print(num)
    msg = email.message_from_bytes(data[0][1])
    print(msg['Subject'])
    msg2 = ascii(msg)
    print(msg2)
    my_file = open("text.txt", "a")
    my_file.close()
msg3 = ""
if msg.is_multipart():
            for part in msg.get_payload():
                if part.get_content_maintype() == 'text' and part.get('Content-Disposition') == None:
                    msg_body = part.get_payload(decode=1)
                    msg3 = ascii(msg_body)
                    my_file = open("text.txt", "a")
                    my_file.write(ascii(msg_body))
                    my_file.close()
print(msg3)
    
con.close()
con.logout()

 from sklearn import linear_model
from sklearn.metrics import r2_score, mean_squared_error
from sklearn.preprocessing import PolynomialFeatures
from sklearn.linear_model import LinearRegression
from sklearn.pipeline import Pipeline
from sklearn.model_selection import cross_val_score
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
    	подкл выборку
df = pd.read_table ("tic/ticdata2000.txt")
cols = ['Col']
x_train = pd.read_table ("tic/ticdata2000.txt", header = None).iloc [0:4000, 0:85]
x_test = pd.read_table ("tic/ticeval2000.txt", header = None)
y_train = df['Col'] = df.iloc[0:4000, 84]
y_test = pd.read_table ("tic/tictgts2000.txt", header = None)
	обучение выборки
regression = linear_model.LinearRegression()
regression.fit (x_train, y_train)
	Предсказание на основе тестового набора
train_predict = regression.predict (x_train)
print('Коэффициенты: \n', regression.coef_)
print("Cреднеквадратичная ошибка: %.2f" % mean_squared_error(y_train, train_predict))
print('Оценка отклонения: %.2f' % r2_score(y_train, train_predict))
	Проверка точности модели по тестовой выборке и запись в файл¶
columns = ['y_pred']
y_pr = pd.DataFrame(train_predict)
y_test = y_test.reset_index(drop = True)
res = pd.concat([y_pr, y_test], axis=1)
res.to_csv("result.txt", index = False)
	[b]Построение полинома[/b]
degrees = [1,2,3]
err = [1,2,3]
for i in range(len(degrees)):
    polynomial_features = PolynomialFeatures(degree = degrees[i],
                                             include_bias = False)
    linear_regression = LinearRegression()
    pipeline = Pipeline([("polynomial_features", polynomial_features),
                         ("linear_regression", linear_regression)])
    pipeline.fit(x_train, y_train)
    scores = cross_val_score(pipeline, x_train, y_train,
                             scoring="neg_mean_squared_error")
    predict_y = pipeline.predict(x_test)
    err[i] = -scores.mean()
    print("\nСтепень: {}\nСреднеквадратичная ошибка = {}(+/- {})".format(degrees[i], -scores.mean(),
                                               scores.std()))
    print('Показатель отклонения: %.3f' % r2_score(y_test, predict_y))

 MemoryError                               Traceback (most recent call last)
<ipython-input-7-4a868cb24d24> in <module>()
     10     pipeline = Pipeline([("polynomial_features", polynomial_features),
     11                          ("linear_regression", linear_regression)])
---> 12     pipeline.fit(x_train, y_train)
     13     scores = cross_val_score(pipeline, x_train, y_train,
     14                              scoring="neg_mean_squared_error")
/usr/local/lib/python3.5/dist-packages/sklearn/pipeline.py in fit(self, X, y, **fit_params)
    257         Xt, fit_params = self._fit(X, y, **fit_params)
    258         if self._final_estimator is not None:
--> 259             self._final_estimator.fit(Xt, y, **fit_params)
    260         return self
    261 
/usr/local/lib/python3.5/dist-packages/sklearn/linear_model/base.py in fit(self, X, y, sample_weight)
    487         X, y, X_offset, y_offset, X_scale = self._preprocess_data(
    488             X, y, fit_intercept=self.fit_intercept, normalize=self.normalize,
--> 489             copy=self.copy_X, sample_weight=sample_weight)
    490 
    491         if sample_weight is not None:
/usr/local/lib/python3.5/dist-packages/sklearn/linear_model/base.py in _preprocess_data(X, y, fit_intercept, normalize, copy, sample_weight, return_mean)
    166 
    167     X = check_array(X, copy=copy, accept_sparse=['csr', 'csc'],
--> 168                     dtype=FLOAT_DTYPES)
    169     y = np.asarray(y, dtype=X.dtype)
    170 
/usr/local/lib/python3.5/dist-packages/sklearn/utils/validation.py in check_array(array, accept_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, warn_on_dtype, estimator)
    400                                       force_all_finite)
    401     else:
--> 402         array = np.array(array, dtype=dtype, order=order, copy=copy)
    403 
    404         if ensure_2d:
MemoryError:

 my @result = $service->method(SOAP::Data->name('MyDate')->value('Value'));

 result=client.service.method({'MyDate':'Value'})