text stringlengths 8 6.05M |
|---|
def dig_sum(n):
return sum([int(i) for i in str(n)])
s_lst = []
for a in range(100):
for b in range(100):
s_lst.append(dig_sum(a**b))
def main():
print(max(s_lst))
if __name__=='__main__':
main() |
# ๅญ็ฌฆไธฒ
str = 'hello'
# ๅฎไนๆนๆณ
def function(arg):
return arg + arg
#่พๅ
ฅ
user_input = raw_input()
#่พๅบ
print user_output
# ๆไปถๆไฝ
|
from django.db import models
# __all__ : ์ธ๋ถ์์ ๋ชจ๋ import * ํ ๋ *์ ๋์์ด ๋๋ ๋ชฉ๋ก.
__all__ = (
'InstagramUser',
)
class InstagramUser(models.Model):
name = models.CharField(
max_length=50,
)
# ๋ด๊ฐ ํ๋ก์ฐํ๋ ์ ์ : following
# ๋๋ฅผ ํ๋ก์ฐํ๋ ์ ์ : follower
# following ํ๋๋ ๋ด๊ฐ ํ๋ก์ฐ ํ๋ ์ฌ๋์ ๋ํ๋.
# ํ๋ก์ฐ ๋นํ๋ ... |
import logging
import socket
import datetime
import time
import os
import threading
import pdb
import sys
import bisect
import traceback
"""
sys.path.insert(0, "./netfilterlib/")
from netfilterqueue import NetfilterQueue
sys.path.append("scapy")
logging.getLogger("scapy.runtime").setLevel(logging.ERROR)
from scapy.all... |
import Chapter3.BinaryClassifier_2
def evaluation(sgd_clf):
from sklearn.model_selection import cross_val_score
result = cross_val_score(sgd_clf, Chapter3.BinaryClassifier_2.X_train, Chapter3.BinaryClassifier_2.y_train_5, #kํด๋ ๊ต์ฐจ ๊ฒ์ฆ ์ฌ์ฉ
cv=3, scoring="accuracy") #3๊ฐ์ ์๋ธ์
์ผ๋ก ๋๋... |
# Date: 10/09/2020
# Author: rohith mulumudy
# Description: manages the file structure
import os
class Files:
def __init__(self, directory):
self.directory = directory
def create_file_structure(self, round_num):
if not os.path.isdir(self.directory):
os.mkdir(self.directory)
if not os.path.isdir("{}/{:0... |
import argparse
from datetime import datetime
import torch
from torch.optim import Adam
from torchsummary import summary # pip install torchsummary
from nn import BengaliNet, CrossEntropySumLoss, LabelSmoothingLoss
from optim import optimize, ReduceLROnPlateau
from utils.data import load_data
from utils.tensorboard ... |
import numpy as np
import pytest
import math
from sklearn.base import clone
from sklearn.ensemble import RandomForestClassifier, RandomForestRegressor
import doubleml as dml
from ._utils import draw_smpls
from ._utils_iivm_manual import fit_iivm, boot_iivm
@pytest.fixture(scope='module',
params=[[... |
import tensorflow as tf
import numpy as np
import os
import sys
from PIL import Image, ImageOps
path = '/content/base/'
def image_preprocessing(filename, x_size, y_size):
im = Image.open(filename)
if filename.endswith('.png'):
im = im.convert('RGB')
downsampled_im = ImageOps.fit(im, (x_size, y_si... |
import numpy as np
from rpy2.robjects import numpy2ri
numpy2ri.activate()
from rpy2.robjects.packages import importr
stepR = importr('stepR')
# x : np.ndarray
# t : np.ndarray
# alpha : float
# -> ([x.dtype], [t.dtype], [(t.dtype, t.dtype)])
def smuce_r(x, t, alpha):
res = stepR.stepFit(x, x=t, alpha=alpha, jumpint=... |
๏ปฟ# coding: utf-8
####################################
#ๆๅใฎไพ(MyButtonใGlobalใงๅ
ฑๆ)
####################################
if "IronPythonใๅใใพใใ" == MyButton.Parent.Text:
MyButton.Text = "Globalsใฎๅฉ็จ ๏ผ1st.py๏ผ"
MyButton.Parent.Text = "IronPython"
elif "IronPython" == MyButton.Parent.Text:
MyButton.Text = "ในใฏใชใใใๅฎ่กใ... |
import pandas as pd
import numpy as np
import glob
import datetime
sonde = pd.read_csv('All Sonde Output.csv', header=0)
GPS = pd.read_csv('Final out.csv', header=0)
CO2 = pd.read_csv('Final CO2.csv', header=0)
sondeCO2 = pd.merge(sonde, CO2, on='time')
print(sondeCO2)
|
from keras.preprocessing.sequence import pad_sequences
from keras.preprocessing.text import Tokenizer
from keras.layers.merge import concatenate
from keras.models import Sequential, Model
from keras.layers import Dense, Embedding, Activation, merge, Input, Lambda, Reshape
from keras.layers import Convolution1D, Flatte... |
"""
realizar la multiplicacion de numeros de
una lista
Tarea 17
"""
def multiplicacion(lista):
res = 1
for i in range(len(lista)):
res = res * lista[i]
return res
prueba1 = [1,2,3,4]#24
prueba2 = [1,2]#2
print(multiplicacion(prueba1))
print(multiplicacion(prueba2)) |
from server import app
from flask import jsonify
from api.model.signboard import Signboard
@app.route('/signboards')
def get_signboard():
r = Signboard.query.all()
return jsonify({"data": r})
@app.route('/signboard/<string:customer_code>/<string:signboard_code>', methods=['PUT'])
def put_signboard(customer_co... |
import os
from sendgrid import SendGridAPIClient
from sendgrid.helpers.mail import Mail
def sendEmail(body):
message = Mail(
from_email='jhalv001@ucr.edu',
to_emails='jhalvorson6687@gmail.com',
subject='RMailbox Package Notification ',
html_content= body)
try:
sg = SendGridAPIClient(os.... |
import sys
import scikit_posthocs as sp
import re
import scipy.stats as ss
from scipy import stats
from numpy import *
alpha=0.05
def check_means(file1, file2):
mean1 = mean(file1)
mean2 = mean(file2)
if mean1 > mean2:
return 1
elif mean1 < mean2:
return -1
else:
return 0
def process_ins... |
a = input("ะะฒะตะดะธ ัััะพะบั:")
if len(a) % 2:
a1 = a[len(a) // 2 + 1:] + a[:len(a) // 2 + 1]
else:
a1 = a[len(a) // 2:] + a[:len(a) // 2]
print(a1)
|
#!/bin/python2.7
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# ... |
import os
import re
import random
import math
import torch
import torch.nn as nn
import numpy as np
from torch.autograd.function import Function
import cv2
INTER_MODE = {'NEAREST': cv2.INTER_NEAREST, 'BILINEAR': cv2.INTER_LINEAR, 'BICUBIC': cv2.INTER_CUBIC}
class CenterLoss(nn.Module):
def __init__(self, num_class... |
# This file is part of beets.
# Copyright 2016, Fabrice Laporte
#
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, ... |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
"""Web presentation DataGroup, UserGroupPerm classes"""
from django import template
#from django import http
from django.shortcuts import render_to_response
# , redirect, get_object_or_404get_list_or_404, HttpResponse
from django.utils.translation import ugettext as _
... |
import networkx as nx
import matplotlib.pyplot as plt
G=nx.Graph()
G.add_node(1)
G.add_node(2)
G.add_node(3)
G.add_edge(1,2)
G.add_edge(2,3)
G.add_edge(3,1)
print(G.nodes) #print the name of edges
nx.draw(G)
plt.show() |
from pyvi import ViTokenizer
path = "document1.txt"
f = open(path,"r",encoding="utf-8")
e = f.read();
s_tt= ViTokenizer.tokenize(e)
fo = open("final.txt","w",encoding="utf-8")
fo.write(s_tt)
f.close()
fo.close() |
import unittest
from katas.kyu_6.simple_sentences import make_sentences
class MakeSentencesTestCase(unittest.TestCase):
def test_equals(self):
self.assertEqual(make_sentences(['hello', 'world']), 'hello world.')
def test_equals_2(self):
self.assertEqual(make_sentences(
['Quick', ... |
import sys
import os
import math
import tempfile
sys.path.insert(0, 'scripts')
sys.path.insert(0, os.path.join("tools", "trees"))
sys.path.insert(0, os.path.join("tools", "print"))
sys.path.insert(0, os.path.join("tools", "families"))
import saved_metrics
import fam
import pickle
from aligned_printer import AlignedPrin... |
import torch
import torchvision
import torch.nn as nn
import numpy as np
import torchvision.transforms as transforms
# ================================================================== #
# Table of Contents #
# =========================================================... |
from typing import Optional, Tuple
from esipy import EsiClient
from waitlist.utility import outgate
from waitlist.utility.config import banned_by_default
from waitlist.utility.sde import add_type_by_id_to_database
from waitlist.storage.database import Constellation, SolarSystem, Station,\
InvType, Account, Charac... |
from common import *
import base64
####file encrypt part
def encrpty_file(file_path, pubkey):
log.debug("-------")
cipher_text = b''
max_length = int(get_max_length(pubkey))
if pubkey:
cipher_public = Crypto.Cipher.PKCS1_v1_5.new(pubkey)
with open(file_path, 'r', encoding='UTF-8') as f... |
import argparse
import os
import pandas as pd
from utils import download_incidents, create_folder, video_to_frames
__author__ = 'roeiherz'
VIDEO_PATH = "/data/Accidents1K/Videos"
INDEX_PATH = "/data/Accidents1K/accident_index.csv"
IMAGE_PATH = "/data/Accidents1K/Images"
def get_video_links(index_path):
"""
... |
#SevenDigitsDrawV2.py
import turtle as p
import time as t
#import turtle,time
def drawGap(): #็ปๅถๆฐ็ ็ฎก้ด้,ๆฏๆฎตๆฐ็ ็ฎกไน้ดไธ่ฟ็ปญ
p.penup()
p.fd(5)
def drawline(draw): #็ปๅถๅๆฎตๆฐ็ ็ฎก
drawGap()
p.pendown() if draw else p.penup()
p.fd(40)
drawGap()
p.right(90)
def drawDigit(digit): #ๆ นๆฎๆฐๅญ็ปๅถไธๆฎตๆฐ็ ็ฎกๅนถๅฐๆตท้พๅณ็งป20ไธชๅ็ด
d... |
# coding: utf-8
# $\newcommand{\xv}{\mathbf{x}}
# \newcommand{\Xv}{\mathbf{X}}
# \newcommand{\yv}{\mathbf{y}}
# \newcommand{\zv}{\mathbf{z}}
# \newcommand{\av}{\mathbf{a}}
# \newcommand{\Wv}{\mathbf{W}}
# \newcommand{\wv}{\mathbf{w}}
# \newcommand{\tv}{\mathbf{t}}
# \newcommand{\Tv}{\mathbf{T}}
# \newcommand{\muv}{\b... |
for str in open("C:/Users/dinesh kumar/Documents/text1.txt"):
print(str,end="")
|
"""
Author : Lily
Data : 2018-09-18
QQ : 339600718
็ง้ขๆฐ Kiehl's Kiehls-s
ๆๅๆ่ทฏ๏ผๅพฎไฟกๅ
ฌไผๅท๏ผๆฟๅฐprovince็id,ๅไธบๅๆฐ๏ผ่ฏทๆฑcity็id,ๆ นๆฎcityid่ฏทๆฑstores็ๆฐๆฎ
getProvince๏ผpost,json๏ผ:http://wx.kiehls.com.cn/KStart/GetProvince
getCity๏ผpost,json,ๅๆฐ๏ผproId๏ผ:http://wx.kiehls.com.cn/KStart/GetCity
getStore๏ผpost,json๏ผๅๆฐ๏ผcity_id: 37,longitude: ,latitude:... |
# Enter script code
keyboard.send_keys("<f6>8") |
from django.contrib.auth.models import User
from django.db import models
class Profile(models.Model):
user = models.OneToOneField(User, on_delete=models.CASCADE)
nickname = models.CharField(max_length=20, default='')
class Meta:
ordering = ['nickname']
def __str__(self):
return self... |
import keras
import pyfor
from keras_retinanet import models
from keras_retinanet.utils.image import read_image_bgr, preprocess_image, resize_image
from keras_retinanet.utils.visualization import draw_box, draw_caption
from keras_retinanet.utils.colors import label_color
import matplotlib.pyplot as plt
import cv2
impor... |
import math
i = 5
j = 2 * i
j = j + 5
print(i)
print(j)
j = j - i
print(j)
print(7 / 2)
print(7 / 3.5)
print(8 + 2.6)
print(9 // 5)
print(9 % 5)
print(3 ** 4)
print(math.sqrt(36))
print(math.log2(2))
print(math.log2(4))
i = 5
print(type(i))
i = 7 * 1
print(type(i))
j = i / 3
print(type(i))
print(type(j))
i = 2 * j ... |
def addfruit(fruit1,price1):
fruit_dict={}
i=0
while i!=len(fruit1):
try:
if fruit1[i].lower() not in fruit_dict:
fruit_dict.update({fruit1[i]:price1[i]})
i+=1
else:
i+=1
raise ValueError
excep... |
class Solution:
def twoCitySchedCost(self, costs: List[List[int]]) -> int:
l = len(costs)//2
costs = sorted(costs, key=lambda i: abs(i[1]-i[0]))
n1 = n2 = tcost = 0
for i in range(len(costs)-1,-1,-1):
if costs[i][0] < costs[i][1]:
if n1<l:
... |
# -*- coding: utf-8 -*-
from django.contrib import admin
from app.customer.models import Customer
class CustomerAdmin(admin.ModelAdmin):
model = Customer
admin.site.register(Customer, CustomerAdmin)
|
import numpy as np
import os
import csv
Tainan_city_id = '467410'
Tainan_city_id2= '467411'
Tainan_county_id = '467420'
Dataset=[]
labellist=['stno','yyyymmdd', 'PS01', 'PS02', 'PS03', 'PS04', 'PS05', 'PS06', 'PS07', 'PS08', 'PS09', 'PS10', 'TX01', 'TX02', 'TX03', 'TX04', 'TX05', 'TX06', 'TD01', 'TD02', 'TD03', 'TD04... |
#!/usr/bin/env python
# Copyright 2017 Google Inc.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or... |
url = 'https://opendart.fss.or.kr/api/list.xml' |
import pyspark
from pyspark import SparkConf, SparkContext
import collections
conf = SparkConf().setAppName("SalaryAnalysis")
sc = SparkContext(conf = conf)
# Detroit Open Data file located https://data.detroitmi.gov/Government/Mayoral-Appointee-Salaries/fwu6-4nb5
lines = sc.textFile("hdfs://cluster-b435-m/wsu/Mayora... |
import os
import sys
import pygame
import requests
from inoutbox import get_key, display_box, ask, main
question = 'https://geocode-maps.yandex.ru/1.x/?apikey=40d1649f-0493-4b70-98ba-98533de7710b&geocode=ะะฒัััะฐะปะธั&format=json'
resp = requests.get(question)
if resp:
resp = resp.json()
cords = resp["response"][... |
from models.data_types import ComplexDataType
class Method(ComplexDataType):
def __init__(self, name, description, result_type_name=None, deprecated=False):
super(Method, self).__init__(name, description, deprecated)
self._result_type_name = result_type_name
self._permissions = {}
@pr... |
# -*- coding: utf-8 -*-
'''
NAPALM Probes
=============
Manages RPM/SLA probes on the network device.
:codeauthor: Mircea Ulinic <mircea@cloudflare.com> & Jerome Fleury <jf@cloudflare.com>
:maturity: new
:depends: napalm
:platform: linux
Dependencies
------------
- :mod:`napalm proxy minion <salt.proxy.napal... |
def add_arguments(parser):
parser.add_argument(
"-d", "--data_file", required=True, help="file or dir containing training data"
)
parser.add_argument(
"-o",
"--out_file",
required=True,
help="file where to save training data in rasa format",
)
parser.add_arg... |
from collections import deque
import random
import numpy as np
class DataMemory():
def __init__(self, max_len=50000):
super(DataMemory, self).__init__()
self.max_len = max_len
self.memory = deque()
def add(self, s_t, a_t, r_t, s_n, terminal):
self.memory.append((s_t, a_t, r_t... |
# -*- coding: utf-8 -*-
"""
Created on Thu Oct 27 12:48:05 2016
@author: wattai
"""
import numpy as np
import matplotlib.pyplot as plt
def gauss_ludgendle(N_iter):
a0 = 1. / np.sqrt(2)
b0 = 1.
s0 = 1.
t0 = 4.
pis = []
for i in range(N_iter):
a1 = np.sqrt(a0*b0)
b1 = (a0 + ... |
import recommendations
from math import sqrt
#import pydilicious
#import maybe #ไฟฎๆน่ฟ็python3็็ๆฌไธๅ ้คไบๅบ่ฏ็ใ
import noway #ๆ่ง่ฐ็จget_popularๅฝๆฐๆ็จๅฐ็้จๅ้ฝๅจ่ฟไบใ
##็ฌฌไบ็ซ ๅ้ข็ๅ
ๅฎน่ฟๆฏๅฏไปฅ็ปง็ปญๅ็ใwww
'''
print(critics['Lisa Rose']['Lady in the Water'])
critics['Toby']['Snakes on a Plane'] = 4.5
print(critics['Toby'])#่พๅบ็้กบๅบๆฏ้ๆบ็ๅใ
a = ... |
from multiprocessing import Pool
from indigox.config import INIT_WITH_GA, NUM_PROCESSES
from indigox.exception import IndigoUnfeasibleComputation
from indigox.misc import (BondOrderAssignment, graph_to_dist_graph, electron_spots,
electrons_to_add, locs_sort, HashBitArray, graph_setup,
... |
try: # try/except block
raise IndexError # raise the exception
except IndexError: # catch the exception
print('got the exception')
print('continuing')
|
from unittest import TestCase
import search
class TestSearch(TestCase):
def test_search_by_name_should_return_empty(self):
contact_data = [
{
"city": "Rennes",
"name": "Ivan Riley",
"country": "Burkina Faso",
"company": "Nonummy... |
import logging
import aioisotp
logging.basicConfig(level=logging.DEBUG)
network = aioisotp.SyncISOTPNetwork(channel='vcan0', interface='virtual', receive_own_messages=True)
server = network.create_sync_connection(0x456, 0x123)
with network.open():
client = network.create_sync_connection(0x123, 0x456)
clien... |
import twitter
from twitter.stream import TwitterStream, Timeout, HeartbeatTimeout, Hangup
from twitter.oauth import OAuth
from twitter.oauth2 import OAuth2, read_bearer_token_file
from twitter.util import printNicely
from string import ascii_letters
import os
import re
import string
import sys
# XXX: Go to http://dev... |
# Generated by Django 2.0.7 on 2018-07-31 09:11
from django.db import migrations, models
import django.utils.datetime_safe
class Migration(migrations.Migration):
dependencies = [
('post_app', '0008_comment_comment'),
]
operations = [
migrations.AlterField(
model_name='file',... |
#!/usr/bin/env python
from manimlib.imports import *
# To watch one of these scenes, run the following:
# python -m manim example_scenes.py SquareToCircle -pl
#
# Use the flat -l for a faster rendering at a lower
# quality.
# Use -s to skip to the end and just save the final frame
# Use the -p to have the animation (... |
from django.conf.urls import include, url
from . import views
urlpatterns = [
url(r'^$', views.QuestionIndexView.as_view(), name='qa_index'),
url(r'^question/(?P<pk>\d+)/$',
views.QuestionDetailView.as_view(), name='qa_detail'),
url(r'^question/(?P<pk>\d+)/(?P<slug>[-_\w]+)/$',
views.Que... |
""" Executed from loop_swap_protocol folder """
import loop_align_updates as la
import pickle
from pyrosetta.rosetta.core.pack.task import TaskFactory
from pyrosetta.rosetta.core.pack.task.operation import \
OperateOnResidueSubset, RestrictAbsentCanonicalAASRLT
from pyrosetta.rosetta.protocols.grafting import... |
# -*- coding: utf-8 -*-
"""Tests for pyss3.cmd_line."""
from pyss3.cmd_line import SS3Prompt, main
from pyss3.server import Server
from pyss3.util import Evaluation
from pyss3 import SS3
from os import path
import pyss3.cmd_line
import pyss3.util
import pytest
import sys
MODEL_NAME = "cmd_test"
DATASET_FOLDER = "data... |
"""Config flow for Toggl integration."""
import logging
import voluptuous as vol
from homeassistant import config_entries, core, exceptions
from .const import DOMAIN # pylint:disable=unused-import
_LOGGER = logging.getLogger(__name__)
DATA_SCHEMA = vol.Schema({"token": str})
class TogglHub:
"""Hub for TogglPy
... |
import numpy as np
def dict_median_scores(dict_list):
median_dict = {}
for key in dict_list.keys():
value = dict_list[key]
value_arr = np.array(value)
if np.isnan(value_arr).any() == True:
median_dict[key] = np.nan
else:
#append_low = np.percentile(value... |
from django.shortcuts import render , redirect , get_object_or_404
from django.http import HttpResponse, Http404
from django.contrib.auth.models import User
from .models import Note
from django.contrib import messages
from django.template.loader import get_template
from django.http import HttpResponse
from .script impo... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Fri Feb 7 20:34:37 2020
@author: adeela
"""
|
# coding: utf-8
# Python script created by Lucas Hale
# Standard Python libraries
from typing import Optional
# http://www.numpy.org/
import numpy as np
# https://github.com/usnistgov/atomman
import atomman as am
import atomman.lammps as lmp
import atomman.unitconvert as uc
from atomman.tools import filltemplate, a... |
import itertools
data = []
"""
Process inputData.txt and put it into the data array.
"""
with open("inputData.txt", "r") as infile:
for line in infile:
data.append(int(line))
valid_permutations = {}
def checkIfValid(permutation):
return sum(permutation) == 150
"""
Main loop
This is the best expla... |
from struct import *
import numpy as np
# I considered using multiprocessing package, but I find this code version is fine.
# Welcome for your version with multiprocessing to make the reading faster.
# from joblib import Parallel, delayed
import multiprocessing
import time
import os
import argparse
from open3d import *... |
import nltk
from nltk.collocations import *
from nltk.metrics import BigramAssocMeasures
import operator
import string
import math
measures = BigramAssocMeasures()
l_ru = []
with open("text_ru.txt", 'r', encoding="utf-8") as f:
for line in f:
for w in nltk.word_tokenize(line.lower()):
if w not in string.punctua... |
from django.db.models import Sum
from django.utils import timezone
from .models import Commission
from apps.jobs.models import Jobs
class CommissionManager(object):
"""
Manager for commission calculations
"""
def getCommUser(self, user):
"""
Returns commission amount due for the use... |
def leiaInt():
red = "\033[1;31m"
print('-' * 20)
while True:
n = str(input('Digite um nรบmero: '))
if n.isnumeric():
n = int(n)
print(f'Voce digitou o nรบmero {n}')
break
else:
print(f'\033[0;31;mERRO! Digite um nรบmero inteiro vรกlido\033... |
import datetime
import hashlib
import json
import rsa
from flask import Flask, jsonify, request
import json
import requests
keys_list = {}
threshold_value = 60
class Blockchain:
def __init__(self):
self.chain = []
self.create_block(proof=1, previous_hash='0', value=0)
def create_block(self, ... |
from common import *
from database import *
from format import *
from site import *
|
import stat
from typing import Dict
from pathlib import Path
class FileStat:
filename: str
relname: str
size: int
mtime: int
FileStatDict = Dict[str, FileStat]
class LocalFileStat(FileStat):
"""
Local filestat
"""
def __init__(self, root: str, path: Path):
stat_output = path.... |
# -*- coding: utf-8 -*-
# Form implementation generated from reading ui file 'form.ui'
#
# Created by: PyQt5 UI code generator 5.11.3
#
# WARNING! All changes made in this file will be lost!
from PyQt5 import QtCore, QtGui, QtWidgets
class Ui_mainWindow(object):
def setupUi(self, mainWindow):
mainWindow.... |
"""@package ScipyMinimize
This package implements the minimize optimisers from SciPy.
@author Dr Franck P. Vidal, Bangor University
@date 5th July 2019
"""
#################################################
# import packages
###################################################
from scipy import optimize
from Solution i... |
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Mon Oct 23 15:45:45 2017
@author: dgratz
"""
from readFile import readFile
import re
from glob import glob
import numpy as np
import matplotlib.pyplot as plt
s = re.compile('/')
u = re.compile('_')
datadir = 'D:/synchrony-data/AllConnAndRand/'
conns = list... |
#Created on February 23, 2017
#@author: rspies@lynkertech.com
# Python 2.7
# This script creates an horizontal bar chart of station data availability
# for precip/temp data sites
import os
import numpy as np
import datetime
from datetime import timedelta
import matplotlib.pyplot as plt
import matplotlib.da... |
""" Placeholder for a synthetic field. """
class SyntheticField:
""" Placeholder for a synthetic field. """
def __init__(self):
""" Store all the parameters for later usage, as well as reference to a synthetic generator. """
def make_sampler(self):
""" Create a sampler to generate pseudo-... |
import numpy as np
e = np.array([11,2,3,4,5,6,12,23,8,21])
print(np.max(e))
print(np.min(e))
print(np.median(e))
print(np.std(e))
|
l,u=map(int, input().split())
li=[]
for i in range(l+1,u):
if i%2==0:
li.append(str(i))
print(" ".join(li))
#prasad
|
import math
from pyspark import SparkContext, SparkConf
import sys
import time
import multiprocessing
from pyspark.mllib.recommendation import ALS, Rating
import heapq
start_time = time.time()
# Model based cf
def model_base_cf(rdd, train_rdd, test_rdd, user_enum, bus_enum):
# Training
ratings_train = train_rdd \
... |
from typing import (
List,
Dict,
Text,
Any,
Optional
)
from rasa.shared.core.events import Event, UserUttered
class UserBotUttered():
def __init__(self,
last_message:UserUttered,
bot_predict_event:Optional[Text]
) -> None:
"""
Crea un evento que contiene... |
#coding:utf-8
#!/usr/bin/env python
from game.routine.pet import pet
from game.routine.skill import skill
from game.routine.equipment import equipment
class luck:
@staticmethod
def check(usr, card, petConf):
"""
ๆฃๆต็ผ
"""
petInfo = petConf[card['cardid']]
luckid = []
for l in petInfo['... |
from fastapi import FastAPI, File, UploadFile
from fastapi.responses import FileResponse,Response
from pydantic import BaseModel
import io
from io import BytesIO
import numpy as np
from PIL import Image
import shutil
import os
os.environ["CUDA_VISIBLE_DEVICES"]="-1"
import tensorflow as tf
... |
import tensorflow as tf
print(tf.__version__)
hello = tf.constant("Hello Tensorflow")
print(hello)
sess = tf.Session()
# ์คํ ํ ๊ฒฐ๊ณผ๊ฐ์ด bytes ํ์
์ด๋ฏ๋ก decode ํ์
result = sess.run(hello)
print(result)
print(result.decode())
a = tf.constant(10)
b = tf.constant(20)
_a, _b = sess.run([a, b])
print(_a, _b)
|
import json
def read_dict(db_name):
try: #little ducktyping -- catches both file not found and file empty (incase it was erased)
with open(db_name, 'r') as f:
print("updating db...db_name")
return json.load(f)
except (FileNotFoundError, ValueError):
print("creating db...... |
from django.urls import path
from jobs.api.views import (JobOfferDetailAPIView,
JobOfferListCreateAPIView)
urlpatterns = [
path("jobs/",
JobOfferListCreateAPIView.as_view(),
name="job-list"),
path("jobs/<int:pk>/",
JobOfferDetailAPIView.as_view(),
... |
"""
https://leetcode.com/problems/number-of-subarrays-with-bounded-maximum/
We are given an array nums of positive integers, and two positive integers left and right (left <= right).
Return the number of (contiguous, non-empty) subarrays such that the value of the maximum array element in that subarray is at least le... |
import json
import logging
import time
import certifi
import urllib3
class BitbucketApiBindings:
"""
Wraps Bitbucket API functions.
"""
def __init__(self, rate_limit: int):
self.__rate_limit = rate_limit
def form_bitbucket_request(self, url: str) -> urllib3.response:
"""
C... |
import cv2
import random
import numpy as np
import matplotlib
matplotlib.use("TkAgg")
from matplotlib import pyplot as plt
import os
grayscale_max = 255
def load_image(filename):
image = cv2.imread(filename, cv2.IMREAD_GRAYSCALE)
return image
def show_image(title, image):
max_val = image.max()
# ... |
import pandas as pd
import sys
outf = sys.argv[1]
score_df = pd.DataFrame()
fids, pids, scores = [], [], []
with open('plink.profile', 'r') as r:
lines = r.readlines()
for line in lines[1:]:
words = line.split()
fids.append(words[0])
pids.append(words[1])
scores.append(float(words[-1]))
score_df['FID'] = fids... |
import random
PATTERNS = [
(1000000000000000, 'xxxxx'),
(-1000000000000000, 'ooooo'),
(10, ' xx '),
(-10, ' oo '),
(10, ' x x '),
(-10, ' o o '),
(10, ' x x '),
(-10, ' o o '),
(100, ' xx '),
(-100, ' oo '),
(-3300000, ' ooo '),
(1100000, ' xxx '),
... |
# encoding: utf-8
'''
This application does a simple NVE+Langevin LAMMPS simulation of spherocylinder-like rods
(defined in a .cfg file) using the "lammps_multistate_rods" library.
The initial locations of the rods are at SC lattice points defined by the input params, and
their orientations are randomly determined at e... |
from prettytable import PrettyTable
from sklearn.metrics import accuracy_score, precision_score, recall_score, f1_score
import utils
class Evaluator(object):
@staticmethod
def evaluate_multi_class(y_preds, y_truths):
pred_labels = set(y_preds)
true_labels = set(y_truths)
all_labels = ... |
# plotting two line diagrams on the same x-axis but different y-axis
# especially useful when you want to superimpose ratio/ percentage against absolute numbers
import numpy as np
import matplotlib.pyplot as plt
import pandas
from textwrap import wrap
from matplotlib.ticker import FuncFormatter
import locale
locale.set... |
import numpy as np
import matplotlib.pyplot as plt
from sklearn.naive_bayes import MultinomialNB
def values_to_bins(data, bins):
return np.digitize(data, bins) - 1
train_images = np.loadtxt('data/train_images.txt') # incarcam imaginile
# incarcam etichetele avand # tipul de date int
train_labels = n... |
TESTAPI_ID = 'testapi_id'
CSRF_TOKEN = 'csrf_token'
ROLE = 'role'
TESTAPI_USERS = ['opnfv-testapi-users']
|
from .models import *
from django.shortcuts import *
from django.views import View
from django.http import HttpResponse, HttpRequest
def check_if_int(number):
try:
return int(number)
except Exception:
return None
def create_person(request):
person = Person()
name = request.POST.get("... |
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