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import torch
import numpy as np
from torch.autograd.functional import jacobian
from multipledispatch import dispatch
from collections.abc import Callable
from scipy.stats import gaussian_kde
def torchProxRecur(pdf_prev, pot_prev, x_prev, x_curr, dt, reg, tol=1e-5, maxiter=300, beta=1., verbose=True):
'''
Proxi... |
# Global maze maze
maze = [
[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1],
[1, 1, 0, 1, 1, 0, 1, 0, 0, 0, 0, 0, 1, 1, 1, 0, 0, 0, 1, 1, 1, 1],
[1, 1, 0, 1, 0, 0, 1, 1, 0, 1, 1, 0, 0, 0, 0, 0, 1, 1, 0, 0, 0, 1],
[1, 1, 0, 0, 0, 1, 0, 1, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1],
... |
import numpy as np
import tensorflow as tf
from utils.layers import *
from utils.initParams import *
from utils.monitor2 import *
# All DNN w/o softmax
# DNN-262144 DNN-262144 DNN-262144 DNN-2
#
####################
# Random Minibatch #
####################
def random_minibatch(data,label,size):
"""
Random choose ... |
number_list = range(100)
def senkei(number_list, num):
found = False
for i in number_list:
if i == num:
found = True
break
return found
print(senkei(number_list, 9))
print(senkei(number_list, 1000))
|
from kiali_qe.tests import IstioConfigPageTest
def test_pagination_feature(kiali_client, openshift_client, browser):
tests = IstioConfigPageTest(
kiali_client=kiali_client, openshift_client=openshift_client, browser=browser)
tests.assert_pagination_feature()
def test_namespaces(kiali_client, openshi... |
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
def filter_data(data, condition):
"""
Remove elements that do not match the condition provided.
Takes a data list as input and returns a filtered list.
Conditions should be a list of strings of the following format:
'<field> <... |
# Copyright (c) 2011-2012 lab126.com
# See COPYING for details.
import time
import threading
from gevent import monkey; monkey.patch_all()
MAX_RUNNING = 30
class Scheduler(threading.Thread):
def __init__(self, handler):
threading.Thread.__init__(self)
self.handler = handler
self.stop = F... |
import os, gc, threading, logging, time
import tensorflow as tf
import cv2 as cv
import numpy as np
from random import randint
from time import sleep
# ambiente
from nes_py.wrappers import JoypadSpace
import gym_super_mario_bros
from gym_super_mario_bros.actions import COMPLEX_MOVEMENT, SIMPLE_MOVEMENT, RIGHT_ONLY
fr... |
# Generated by Django 2.1.7 on 2019-06-08 18:55
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('app2', '0008_auto_20190604_1534'),
]
operations = [
migrations.RenameField(
model_name='costs',
old_name='equipment'... |
from flask import Flask, Response, render_template, jsonify, request
from flask import Flask
import mysql.connector
db=mysql.connector.connect(host="localhost",user="root",password="dpsdpsdps",database="project")
cursor=db.cursor()
cursor.execute("select count(acc_no) from customer")
result=cursor.fetchall()
i=re... |
from django.conf import settings
from django.db import models
import datetime
import json
emoji_choices = (
('h', 'healthy'),
('s', 'sick'),
('y', 'sleepy'),
('c', 'cough'),
('f', 'fever'),
('u', 'flu'),
('n', 'nauseous'),
('l', 'sore throat'),
('r', 'runnynose'),
('b', 'body ac... |
from __future__ import annotations
from collections.abc import Iterator
from .abc import AbcIterator, AbcIteratorFactory
from fibonacci.collection import AbcLinearCollection
class LinearIterator(AbcIterator):
"""Iterates over given indices of the collection
"""
__slots__ = ["_collection", "_indices"]
... |
VULN_RES_JSON = {
"results": [
{
"affects": {
"vuln_techs": [
{
"created_on": "2022-01-20T20:56:48.000Z",
"key": "cpe:/a:f5:big-ip:16.1.1",
"last_modified": "2022-01-20T20:56:48.000Z",
... |
import json
import itertools
import re
from django.db import models
from django.conf import settings
from django.db.models.signals import pre_delete
from django.dispatch import receiver
from django.core.exceptions import ObjectDoesNotExist
from audit_log.models.managers import AuditLog
import tutelary.engine as engine
... |
import pickle
from unittest import mock
import numpy as np
import pytest
import tensorflow as tf
from metarl.tf.models import GaussianCNNModel
from tests.fixtures import TfGraphTestCase
class TestGaussianCNNModel(TfGraphTestCase):
def setup_method(self):
super().setup_method()
self.batch_size = ... |
import random
print('Welocome to guess a nubmber! \nToday might just be your lucky day, Come on take a guess ;)')
print( )
print('='*8,'start','='*8)
user_input = int(input('Enter a number between 0 - 20: '))
lucky_number = random.randint(0, 21)
def guess_a_num(user_input, lucky_number):
for i... |
import os
from gtts import gTTS
import datetime
import json
import time
import requests
import argparse
import logging
from bs4 import BeautifulSoup
from tabulate import tabulate
URL = 'https://www.mohfw.gov.in/'
FILE_NAME = 'corona_india_dashboard.json'
changed = False
r = requests.get(URL)
htmlContent = r.text
def ... |
#!/usr/bin/python
# -*- coding: utf-8 -*-
# version: 20110704
# By Dennis Drescher (dennis_drescher at sil.org)
###############################################################################
######################### Description/Documentation ###########################
##############################################... |
def NewtonRafson(f,dfdx, initial_guess,e):
x = initial_guess
for i in range(1, 100):
y = float(f(x))
if abs(y) < e:
break
x = x - (y / dfdx(x))
i += 1
return x
def func(x):
y = x**2 - 20 * x + 100
return y
def derivative(x):
y = 2 * ... |
from fastapi import APIRouter, HTTPException
from f5_token import get_token
import f5_related.schemas
from fastapi.encoders import jsonable_encoder
import requests
import json
from f5_related.to_excel import to_excel
from config import Setting
from app_logging import logger
router = APIRouter()
settings = Setting()
ro... |
filenames = [
"utils.js",
"geometry.js",
"axes.js",
"plot.js",
"point.js",
"line.js",
"tick.js",
"arrow.js",
"measure.js",
"angle.js",
"circle.js",
"polygon.js",
"text.js",
"controlpoint.js",
"button.js",
]
with open('mavis.js', 'w') as outfile:
for fname in filenames:
with open(fname) as infile:
... |
# coding: utf-8
try:
from urllib.parse import urljoin
except ImportError: # Python 2.7
from urlparse import urljoin # noqa
try:
from unittest.mock import patch
except ImportError: # Python < 3.3
try:
from mock import patch # noqa
except ImportError:
patch = None
try:
fr... |
import time
inicio = time.time()
def insertionSort(arr):
for i in range(1, len(arr)):
key = arr[i]
j = i-1
while j >=0 and key < arr[j] :
arr[j+1] = arr[j]
j -= 1
arr[j+1] = key
from random import sample
random = sample(range(0, 8500), 8500) ... |
# -*- coding: utf-8 -*-
"""
Created on Tue Apr 16 16:35:32 2019
@brief: use pd to load data to list from txt file
use nx and plt to draw figures
@author: 冯准生
"""
import networkx as nx
import matplotlib.pyplot as plt
import pandas as pd #pandas,强大的数据处理能力
#处理数据简洁，两行代码搞定
sourse_data=pd.read_csv("usair.txt",... |
# БСБО-05-19 Савранский С.
import math
i = 0
calc_dist = lambda x, y: ((x - 0.75) ** 2 + (y - 0) ** 2) ** .5
while i < 2 * math.pi:
x = math.cos(i) ** 3
y = math.sin(i) ** 3
if i == 0:
dist = calc_dist(x, y)
elif (new_dist := calc_dist(x, y)) < dist:
dist = new_dist
i += .0001
|
import random
w = input("Enter a number between 0 and 5 ")
x = random.randrange(0,5 )
if w == x:
print("You won")
else:
print("You loose")
print(x) |
# -*- coding: utf-8 -*-
# __author__ = 'huang_wang'
import numpy as np
def segment_dist(points, pv):
n = len(pv) - 1
d = np.zeros((len(points), n))
j = 0
for p in points:
for i in range(n):
a = pv[i]
b = pv[i+1]
ab = b - a
ap = p - a
... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from collections import defaultdict
def read(f):
t = int(f.next())
for i in xrange(t):
n, m = map(int, f.next().strip().split())
D = [f.next().strip() for j in xrange(n)]
L = [f.next().strip() for j in xrange(m)]
yield D, L
def ext... |
from lib.mixpanel_test import MixpanelTest
mp = MixpanelTest()
data = mp.request(
funnel_id=809349,
seg_property='artist-page-interface',
from_date='2014-08-28'
)
mp.test(data, control='fillwidth', variations=['filter', 'filter_carousel'])
|
#!/usr/bin/env python
import pyfwk
from pyfi.calendar.calendar.model import CalendarModel
# ----------------------------CATEGORY-OBJECT-----------------------------#
class Calendar(pyfwk.Object):
id = None
year = None
month = None
day = None
weekday = None
session = None
mktopen = None
... |
import pygame
import csv
import socket
import pickle
def rotaciona_alt(image, angle):
orig_rect = image.get_rect()
rot_image = pygame.transform.rotate(image, angle)
rot_rect = orig_rect.copy()
rot_rect.center = rot_image.get_rect().center
rot_image = rot_image.subsurface(rot_rect).copy()
retur... |
'''
Created on Apr 11, 2011
@author: virushunter2
'''
import wx
from Bio import SeqIO
def main(infile, threshold, length):
length = int(length)
threshold = int(threshold)
filters = 'FASTA files (*.fasta; *.fastq; *.fa; *.fna; *.seq; *.txt)|*.fasta;*.fastq;*.fa;*.fna;*.seq;*.txt'
dialog = wx.FileDialog... |
import re
from bs4 import BeautifulSoup
import requests
url = 'https://baike.baidu.com/item/Python/407313?fr=aladdin'
headers = {
'user-agent':
'Mozilla/5.0 (Windows NT 6.1; Win64; x64) '
'AppleWebKit/537.36 (KHTML, like Gecko) '
'Chrome/75.0.3770.100 Safari/537.36'
... |
def comp(char):
cmpl=''
if char=='+':
cmpl='-'
else:
cmpl='+'
return cmpl
def revcomp(string,end):
out=""
for i in range(end-1,-1,-1):
out+=comp(str(list(string)[i]))
out+=string[end:len(string)]
return out
t=int(raw_input())
for i in range(t):
stack=raw_input()
happy=0
count=0
while happy<len(stack... |
#coding:utf-8
import numpy as np
from load_data_from_database import query_kdata
import sys
sys.path.append('/mnt/aidata/QuantitativePlatform/lib')
import time
import datetime
#as get_model_name
from tool import Model
from tool import get_model_name
import config
import os
import common_tool
import pandas as pd
import... |
from functools import wraps
from typing import Any, Callable, List
import torch
from torch import nn
from kornia.core import Tensor
def image_to_tensor(image: Any, keepdim: bool = True) -> Tensor:
"""Convert a numpy image to a PyTorch 4d tensor image.
Args:
image: image of the form :math:`(H, W, C)... |
import os
import argparse
import re
from collections import Counter
import csv
import numpy as np
from scipy import stats
import matplotlib
matplotlib.use('QT4Agg')
import matplotlib.pyplot as plt
from functools import wraps
SPLIT = re.compile('\W+')
def get_name(directory):
return os.path.split(directory[:-1... |
import pygame
from pygame.sprite import Sprite
class Ship_left(Sprite):
"""Initialize the ship and set its starting position."""
def __init__(self, screen):
super().__init__()
self.screen = screen
self.image = pygame.image.load('images/ship_left.bmp')
self.rect = self.image.g... |
import csv
import pickle
import networkx as nx
import matplotlib.pyplot as plt
g_base='../../../../datafiles/data-ITDK/'
dataset="data-Geolocation/"
dataset="data-ASN/"
if dataset=="data-Geolocation/":
years=[2015, 2016]
base = "../../../../resultfiles/Results-Geolocation/involved/"
ds='2016-09'
elif da... |
import pexpect
import string
class CommandExecuter:
def Run(self, command_line, remote_ip, user, password, run_background):
cmd = self.FormatCommand(command_line, remote_ip, user)
print 'Running %s' % cmd
expect = self.Spawn(cmd)
return self.DoRun(expect, password, run_background)
def ... |
import heapq
import pandas as pd
import copy
from rltk.record import Record, get_property_names
from rltk.evaluation.ground_truth import GroundTruth
from scipy.optimize import linear_sum_assignment
from typing import Any
class Trial(object):
"""
Trial stores the calculated result for further evaluation.
... |
import json
import requests
import hashlib
import base64
import bcrypt
from cryptography.hazmat.backends import default_backend
from cryptography.hazmat.primitives import hashes, hmac
def response(password, salt, challenge):
key = bcrypt.hashpw(password.encode('utf-8'), salt.encode('utf-8'))
h = hmac.HMAC(key... |
from __future__ import print_function
from Configuration.PyReleaseValidation.relval_steps import Matrix, InputInfo, Steps
import os
import json
import collections
workflows = Matrix()
steps = Steps()
def get_json_files():
cwd = os.path.join(os.getcwd(), "json_data")
if not os.path.exists(cwd):
retu... |
import urllib.request
import urllib.parse
import re
def handle_request(url,page=None):
#拼接出来指定url
if page != None:
url = url + str(page) + '.html'
headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/64.0.3282.140 Safari/537.36 Ed... |
#!/usr/bin/python3
#from capstoneg08_client.Client import Client
class ServerMessageHandler():
def __init__(self, myClient):
self.myClient = myClient
def handleServerMessage(self, msg):
serverMessage = ""
serverMessage = msg.decode()
print(serverMessage);
return
... |
from flask import Flask, render_template, url_for, session, request, redirect, g, flash
import fivemin
import pandas as pd
import sys
from bs4 import BeautifulSoup
app = Flask(__name__)
app.config['DEBUG'] = True
app.config['PROPAGATE_EXCEPTIONS'] = True
app.secret_key = 'mr. secrets'
@app.route('/')
def index():
... |
import pathmagic # noqa
import os
from maskrcnn.lib.config import cfg
from maskrcnn.lib.utils.io_utils import read_image
from maskrcnn.lib.models.mask_rcnn import MaskRCNN
image_path = os.path.join("..", "data", "COCO_val2014_000000018928.jpg")
image = read_image(image_path)
print("image shape", image.shape)
model =... |
def custom_fibonacci(n):
if n == 1:
return 1
elif n == 2:
return 2
else:
return custom_fibonacci(n-1) + custom_fibonacci(n-2)
"""custom fibonnaci(5) = (1+1) + custom_custom_fibonacci(4)
= 2 + 1 + custom fibonacci(3)
= 3 + 2 + custom ... |
import pandas as pd
import numpy as np
import warnings
import random
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
warnings.filterwarnings('ignore')
def read_file():
data = pd.read_csv('data.cs... |
import types
import itertools
import numpy
from enum import Enum, unique
from functools import reduce
from pycsp3.classes.auxiliary.ptypes import auto
from pycsp3.classes.main.variables import Variable, NotVariable, NegVariable
from pycsp3.classes import main
from pycsp3.dashboard import options
from pycsp3.tools.uti... |
# Generated by Django 3.2.7 on 2021-09-28 10:05
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('student', '0004_alter_city_thumbnail'),
]
operations = [
migrations.RemoveField(
model_name='city',
name='District',
... |
'''
Dnspython is a
DNS toolkit for Python. It can be used for queries,
zone transfers, dynamic updates, nameserver testing,
and many other things. https://www.dnspython.org/
'''
import dns.reversename
import dns.resolver
import pandas as pd
data = pd.read_csv("./scanned_hosts.csv")
print(data)
for index, row... |
import discord
from discord.ext import commands
from core.classes import Cog_Extension
import json
with open('setting.json','r', encoding='utf8') as jfile:
jdata = json.load(jfile)
class Event(Cog_Extension):
def __init__(self,bot):
self.bot = bot
@commands.Cog.listener()
async def on_member... |
# Program to count the no. of reference pointing to same object
import sys
class Animal:
pass
ref1 = Animal()
ref2 = ref1
print("Reference count is", sys.getrefcount(ref1))
ref3 = ref2
ref4 = ref3
print("Reference count is", sys.getrefcount(ref1))
|
#!/usr/bin/python3
import sys
import minify_html
if (len(sys.argv) <= 2):
print('Usage: minify.py input.html output.html')
sys.exit(1)
input_file = open(sys.argv[1], 'r')
output_file = open(sys.argv[2], 'w')
html = input_file.read()
input_file.close()
html_minified = minify_html.minify(html, minify_css=Tru... |
"""
This script vectorizes the body and title data, using the MeanEmbeddingVectorizer
from w2v_vectorizers, using the chosen best word2vec model for the datasets used
for the final logistic model..
"""
from w2v_vectorizers import MeanEmbeddingVectorizer
from gensim.models.word2vec import Word2Vec
from sklearn.externals... |
# Crear el diccionario "frutas"
# frutas = {"manzana" : "apple","naranja":"orange","platano":"banana","limon":"lemon"}
# Grabar esta estructura de datos "frutas" en un fichero binario "fichero.pckl"
# Ya que es un fichero de texto solo se guardaran caracteres, pero no se pueden guardar guardar estas estructuras
# Rec... |
# -*- encoding: cp949 -*-
def getpartialmatch(N):
m = len(N)
pi = [0] * m
begin = 1;
matched = 0
while begin + matched < m:
if N[begin + matched] == N[matched]:
matched += 1
pi[begin + matched - 1] = matched
else:
if matched == 0:
b... |
#!/usr/bin/python2
import os
import sys
import commands as cmd
# jobtracker_ip ,typeOfScheduler , include host, exclude host , minMap, MinReducer, MaxRunningJobs
def main(argv):
if len(argv) != 3:
sys.exit("Usage mapred-site_modifier.py <tasktracker/jobtracker(T/J)> <jobtracker_ip> <FIFO/FAIR>")
if ... |
from flask import Flask, url_for, jsonify, Response, render_template, send_from_directory
import config
import json
from capture import capture
import requests
from random import randint
import os, time
import subprocess
import picamera, binascii
from multiprocessing import Process
from flask import make_response
fr... |
# coding: utf-8
from __future__ import unicode_literals
import time
import random
import hashlib
import requests
import datetime
DEFAULT_CONTENT_TYPE = 'application/x-www-form-urlencoded'
class NeteaseMessage(object):
def __init__(self, key, secret, tpl_id):
self._key = key
self._secret = secret... |
# A program to show a nested for loop.
outer_loop_total = 0
inner_loop_total = 0
countries = ["America", "England", "India", "China"]
capitals = ["Washington", "London", "New Delhi", "Beijing"]
for country_to_check in countries:
outer_loop_total += 1
for city_to_check in capitals:
inner_loop_total += 1
if c... |
# dataset
datasetDir = '/home/lili/datasets/coco'
dataType = 'val'
imgDir = '{}/img/{}'.format(datasetDir, dataType)
txtDir = '{}/gt/{}/txt'.format(datasetDir, dataType)
figDir = '{}/fig/{}'.format(datasetDir, dataType)
import os
import re
import skimage.io as io
if not os.path.exists(figDir):
os.makedirs(figD... |
from django.shortcuts import render
from .models import Project
def index(request):
projects = Project.objects.all()
return render(request, 'projects/index.html', {'projects': projects})
def detail(request):
project = Project.objects.get(pk=100)
return render(request, 'projects/detail.html', {'proje... |
from django.contrib.auth.models import User, Group
from django.http import HttpResponseRedirect
from app.models import Request, Word, TotalIndex, WordInRequest, WordPositionsInRequest, RequestResponse, StopWord
from rest_framework import viewsets
from app.serializers import UserSerializer, GroupSerializer, RequestSeri... |
import os
import glob
import time
from datetime import datetime
import random
import argparse
import numpy as np
from scipy.stats import t
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
from utils import make_batch, make_batch_rev, load_data
from models import FCGAT, RPA... |
# -*- coding: utf-8 -*-
import sys
import traceback
import re
import json
import os
import threading
import xmlrpclib
import markdown2
import sublime, sublime_plugin
'''文件头样例
<!--iblog
{
"title":"博客标题写在这里",
"categories":"博客分类",
"tags":"标签",
"publish":"false",
"blog_id":"3452965"
}
-->
'''
HEADER_T... |
import sys
sys.path.append("../")
import pygame
class Sound:
def __init__(self, background='../sounds/background.ogg', volume=0.3):
self.transformed = pygame.mixer.Sound('../sounds/transformed.wav')
self.normal_background = pygame.mixer.Sound(background)
self.normal_background.set_vo... |
# Definition for a binary tree node.
# class TreeNode(object):
# def __init__(self, val=0, left=None, right=None):
# self.val = val
# self.left = left
# self.right = right
class Solution(object):
def maxAncestorDiff(self, root):
"""
:type root: TreeNode
:rtype: in... |
import json
import requests
import webbrowser
class Media:
def __init__(self, title="No Title", author="No Author", year="No Release Year", diction=None, url="No url"):
self.diction = diction
if diction == None:
self.title = title
self.author = author
self.year = year
else:
if "trackName" in dicti... |
'''
Created on 11 set 2017
@author: davide
'''
# Image loading
import matplotlib.image as mpimg
import os
import seaborn as sns
sns.set()
image = mpimg.imread('./../images/MarshOrchid.jpg')
print(image.shape)
import matplotlib.pyplot as plt
plt.imshow(image)
plt.show()
# Transpose the image
... |
##
# This software was developed and / or modified by Raytheon Company,
# pursuant to Contract DG133W-05-CQ-1067 with the US Government.
#
# U.S. EXPORT CONTROLLED TECHNICAL DATA
# This software product contains export-restricted data whose
# export/transfer/disclosure is restricted by U.S. law. Dissemination
# to ... |
import pandas as pd
from twilio.rest import Client
account_sid = "AC3f920516d3d046cf1a9c0be5eeb97495"
auth_token = "7158a8bedcbc9548ce519c5084a2339f"
client = Client(account_sid, auth_token)
# TODO: Instalar:
# pandas
# openpyxl
# twilio
# TODO: Passo a passo da solução.
# TODO: Abrir os 6 arquivos do excel.
lista... |
import argparse
import http.server
import json
import logging
import paho.mqtt.client as mqtt
import re
import signal
import socketserver
import sys
import time
from threading import Thread, Lock, Event, current_thread
DEFAULT_HTTP_SERVER_PORT = 8080
# HTTP server runs on localhost
DEFAULT_MQTT_URL = "localhost"
DEFAU... |
import os
import sys
from textwrap import shorten
from getkey import getkey # cross-platform module for getting single keypresses
directory = sys.argv[1]
def process_file(file_path):
with open(file_path, "r+") as f:
lines = f.readlines()
modified = False
for i, line in enumerate(lines):
... |
#---------------------------------------------------------------------------
#
#This testcase tests AllPair Pattern APIs
#1. import
#2. name
#3. permutation
#4. set1_elements
#5. set2_elements
#6. set1_initialization()
#7. set2_initialization()
#8. element_comparision()
#
#---------------------------------------------... |
from scipy.stats import norm
import numpy as np
data = np.array([ 12.1085187 , 12.10867427, 11.21137858, 10.01311363,
10.79744224, 13.19280269, 12.44086123, 11.88810057,
10.70064104, 11.50382741])
def log_likelihood_normal_two_parameters(mu, sigma_sq, data_in):
"""
C... |
import soundfile
import time
import numpy as np
import os
class AudioConverter:
CHUNK_SIZE = 1024
VOLUME_PERCENTAGE = 100
def __init__(self):
self.sound_file = None
self.buffer = []
self.path = None
self.sound_info = None
self.finished = False
self.loaded = False
self.chunk_set = False
self.proces... |
'''
Title : Set .discard(), .remove() & .pop()
Subdomain : Sets
Domain : Python
Author : codeperfectplus
Created : 17 January 2020
'''
n = int(input())
s = set(map(int, input().split()))
methods = {
'pop' : s.pop,
'remove' : s.remove,
'discard' : s.discard
}
for _ in range(int(input())):
... |
# -*- coding: utf-8 -*-
from django.conf.urls import include, url, patterns
from django.contrib import admin
from item.views import *
# from django.conf import settings
from django.conf.urls.static import static
from django.conf import settings as st
import os
from django.contrib.staticfiles.urls import staticfiles_ur... |
class Solution(object):
def largestDivisibleSubset(self, nums):
"""
:type nums: List[int]
:rtype: List[int]
"""
nums.sort()
table=[1 for i in range(len(nums))]
aux = [i for i in range(len(nums))]
for i in range(1,len(nums)):
for j in range(... |
import pandas as pd
import logging
import random
import numpy as np
import re
import os
from framework import Task, ModelTrainer, FeatureEngineering, Evaluation, DataCuration
from infer_bert_qa import get_qa_inference
class TaskQA(Task):
def __init__(self, config):
self.config = config
class Feature... |
import shutil
from utils import *
def windows_installer_cmake(properties):
repository_path = properties["repository_path"]
verbose_output = properties["verbose"]
debug = properties["debug"]
if debug:
print(" ! Debug build not supported on Windows")
version = "3.9.3"
url = "https://cmake.org/files/v3.... |
from .helpers import *
from .hparams import HParams, copy_hparams, save_config, load_config, print_config
from .slack_bot import Notifier
from .image_transforms import * |
import sys
import cj_function_lib as cj
import init_file as variables
import mdbtools as mdt
#print variables.ProjMDB
#print variables.QSWAT_MDB
Watersheds = cj.extract_table_from_mdb(variables.ProjMDB, 'Watershed', variables.path + "\\watershed.tmp~")
pndrgn = cj.extract_table_from_mdb(variables.QSWAT_MDB, 'pndrng... |
import yaml, logging, os, shutil
from subprocess import call as _call
import re
from pprint import pformat
# Set this to avoid accidentally issuing docker commands
NO_CALL = False
def call(cmd):
logging.info(cmd)
if NO_CALL:
return
_call(cmd)
def docker_retag_image(old, new):
cmd = ['docker'... |
import createRandomTree as tree
import Queue
'''BFS uses a FIFO structure (queue)'''
numNodes = 50
randSeed = 1
goal = 6 #arbitrarily defined
root = tree.getBinaryTree(numNodes, randSeed)
queue = Queue.Queue()
queue.put(root)
counter = 0
while not queue.empty():
print 'Iteration ' + str(counter)
counter += ... |
import unittest as ut
import ros.main
class TestCode(ut.TestCase):
def test_colourcode(self):
self.assertEqual(ros.main.colourcode('#212121', 'hex', True), '#212121')
def test_changecolour(self):
self.assertEqual(
str(ros.main.changecolour('#212121', 'red', 10)), '#192121')
... |
PRODUCT_NAME = "//h1[@itemprop='name']"
QUANTITY_WANTED = "//input[@id='quantity_wanted']"
PLUS_ICON = "//i[@class='icon-plus']"
SIZE_DROPDOWN = "//div[@class='attribute_list']/div[@id='uniform-group_1']/select"
COLOUR_SELECT = "//ul[@id='color_to_pick_list']/li/a[@name='~~~']"
COLOUR_SELECTED ... |
# Gaussian Quadrature Formula to compute definite integral of a function
# Input required : function
from math import *
def f(x, function):
return eval(function)
def parseCompile(equation):
if 'e^' in equation:
equation = equation.replace('e^', 'exp')
if '^' in equation:
equation = equa... |
"""
Contains internal configurations that the user might want to act upon.
"""
# REFACTOR: [better confs]: move to the configs .yaml
# Defines what nodes are to be masked to avoid conduction overload of non-informative nodes
# Their edges are not very hight necessarily, but they are highly central in the Reactome
# ph... |
import zope.schema
import zope.interface
from plonezoho.remoteeditor import MessageFactory as _
class IRemoteAPI(zope.interface.Interface):
apikey = zope.schema.TextLine(
title=_(u"API key"),
description=_("TODO: needs description from where to get apikey!"))
skey = zope.schema.TextLine(
... |
import play, time
class Game_map:
def __init__(self, image_name, walls, dots, fruit):
self.score = 0
self.fruit = fruit
self.full_image = play.new_image(image=image_name, x=0, y=0, size=50)
self.full_image.transparency = 0
self.walls = walls
self.dots = dots
... |
from pathlib import Path
import csv
# ***Working with CSV Files***
with open('data.csv', 'w') as file:
writer = csv.writer(file)
writer.writerow(['transaction_id', 'product_id', 'price'])
writer.writerow(['1000', '1', '5'])
writer.writerow(['1001', '2', '15'])
with open('data.csv') as file:
reade... |
import urllib.request
import re
link=urllib.request.urlopen('http://ramakrishnavivekananda.info/')
f=link.read()
D = f.decode('utf-8')
x=re.sub('<.*?>','',D)
with open('G:/python ex/file.txt','w',encoding='utf-8') as fwrite:
fwrite.write(x)
|
#lex_auth_012693825794351104168
def find_common_characters(msg1, msg2):
a = ""
for ch in msg1:
if ch in msg2 and ch != "" and ch not in a:
a += ch
if len(a):
return a
return -1
#Provide different values for msg1,msg2 and test your program
msg1 = "I like Python"
msg2 = "Jav... |
import os
path="E:/data2/zhengchang/"
name="zhengchang"
for root, child, files in os.walk(path):
i=1
os.chdir(path)
for each in files:
os.rename(each, name+str(i)+".avi")
i=i+1 |
import numpy as np
import matplotlib.pyplot as plt
# 先在四个中心点附近产生一堆数据
real_center = [(1, 1), (1, 2), (2, 2), (2, 1)]
point_number = 50
points_x = []
points_y = []
for center in real_center:
offset_x, offset_y = np.random.randn(point_number) * 0.3, np.random.randn(point_number) * 0.25
x_val, y_val = center[0] ... |
import athlete
import coach
import predefined
choices = ("athlete", "coach", "predefined", "exit")
def main():
choice = "x"
print("\nETPG® | The training schedule program for any endurance trainer and athlete")
while (choice not in choices) or choice !="exit":
choice = str(input("\nGeneral Menu ... |
import numpy as np
from matplotlib import pyplot as plt
from matplotlib import animation
# First set up the figure, the axis, and the plot element we want to animate
fig = plt.figure()
ax = plt.axes(xlim=(0, 500), ylim=(-0.3, 0.3))
line, = ax.plot([], [], lw=2)
def PIB_Func(x, n, L):
return ... |
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