seq_id string | text string | repo_name string | sub_path string | file_name string | file_ext string | file_size_in_byte int64 | program_lang string | lang string | doc_type string | stars int64 | dataset string | pt string | api list |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
269149666 | import numpy as np
import matplotlib.pyplot as plt
infile = open('output.txt','r')
header_size = int(infile.readline().rstrip())
m = int(infile.readline().rstrip())
h = float(infile.readline().rstrip())
numproc = int(infile.readline().rstrip())
infile.close()
data = np.genfromtxt('output.txt',skip_header=header_size)... | null | week3/plotter.py | plotter.py | py | 836 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.genfromtxt",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "numpy.linspace",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "numpy.meshgrid",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot.subp... |
515376614 | ''' Predict demand for bikes and empty docks based on historical data '''
import pandas as pd
import numpy as np
from sklearn.tree import DecisionTreeRegressor
from sklearn.metrics import mean_absolute_error
#=====================================================================
# Function to learn decision tree regre... | null | models/predict_demand.py | predict_demand.py | py | 3,397 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pandas.read_csv",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "sklearn.tree.DecisionTreeRegressor",
"line_number": 58,
"usage_type": "call"
},
{
"api_name": "sklearn.tree.DecisionTreeRegressor",
"line_number": 59,
"usage_type": "call"
}
] |
47487292 | #!/usr/bin/python3
from __future__ import print_function
from message.message_pb2 import RequestV1, RequestV2, ResponseV1, ResponseV2
import numpy as np
import yaml
from keras.models import model_from_yaml, model_from_json
import os
import socket
from struct import pack
def load_model_and_weights(model_name = 'model.1... | null | server.py | server.py | py | 5,272 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "os.path.join",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 12,
"usage_type": "attribute"
},
{
"api_name": "keras.models.model_from_json",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "os.path.join",... |
167967850 | # Quick Python Exercise: Requests and Collections :flag-fr:
# Given this resource: https://github.com/thm/uinames , read the documentation and extract 25 random names, both male and female, from France. Also count all the names by gender
# Example Output:
# {'name': 'Maxence', 'surname': 'Prevost', 'gender': 'male', 'r... | null | Modules/collections_module/dev_exercise/elijah_france.py | elijah_france.py | py | 1,197 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "requests.get",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "json.dumps",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "collections.Counter",
"line_number": 21,
"usage_type": "call"
}
] |
517124325 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2018/2/5 14:35
# @Author : zhujinghui
# @File : binance_websocket.py
# @Software: PyCharm
import pymysql
from binance.client import Client
from binance.websockets import BinanceSocketManager
from DBUtils.PooledDB import PooledDB
from decimal impor... | null | binance/dev/order_book_new.py | order_book_new.py | py | 5,385 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "binance.client.Client",
"line_number": 31,
"usage_type": "call"
},
{
"api_name": "binance.websockets.BinanceSocketManager",
"line_number": 32,
"usage_type": "call"
},
{
"api_name": "DBUtils.PooledDB.PooledDB",
"line_number": 37,
"usage_type": "call"
}
] |
148849443 | from time import sleep
import math
import Map
import numpy as np
from pygame.math import Vector2
from scipy.spatial.distance import euclidean
import scipy.spatial.distance as ds
from geometry import line_intersection, line
class Car:
def __init__(self, x, y, angle=270.0, length=10):
self.position = Vecto... | null | geneticAlgorithmLearning/Car.py | Car.py | py | 4,835 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pygame.math.Vector2",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "pygame.math.Vector2",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "numpy.around",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "numpy.around",
... |
533594234 | from __future__ import absolute_import, print_function, unicode_literals
from ..tlib import NamedTemporaryFile, TestCase
from ..impl.bool import FF, TT
from ..impl.path import Y_path
from pathlib import Path
def setup():
p = Y_path(0, r'1')
s = p.fp()
j = Path(r'0', r'1').__str__()
return p, s, j
c... | null | root/test/_4path.py | _4path.py | py | 923 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "impl.path.Y_path",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "pathlib.Path",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "tlib.TestCase",
"line_number": 16,
"usage_type": "name"
},
{
"api_name": "impl.bool.FF",
"line_... |
157586531 | import os
import pickle, json, datetime
import matplotlib
import matplotlib.dates as md
import matplotlib.pyplot as plt
import matplotlib.patches as mp
import numpy as np
import fox_telem
# Globals
years = md.YearLocator() # every year
months = md.MonthLocator(interval=1) # every month
weeks = md.WeekdayLocator(by... | null | plot/upsets_mplt.py | upsets_mplt.py | py | 1,726 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "matplotlib.dates.YearLocator",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "matplotlib.dates",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "matplotlib.dates.MonthLocator",
"line_number": 13,
"usage_type": "call"
},
{
"api_n... |
153490113 | from segbuild import seg_builder
import argparse
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='hey')
parser.add_argument('-cidr', dest='cidr', help='Please enter a CIDR (ex: 192.168.1.0/24)')
args = parser.parse_args()
if not args.cidr:
parser.print_help()
exit(1)
seg_bu... | null | langs/python/tests/networking/segmain.py | segmain.py | py | 337 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "segbuild.seg_builder",
"line_number": 14,
"usage_type": "call"
}
] |
425226333 | from data.dataAPI import dataAPI
from models.contracts import Contract
class ContractLogic:
def __init__(self):
self.data = dataAPI()
def all_contracts(self):
return self.data.get_contracts()
def create_contract(self, a_list):
new_contract = Contract(self.data.new_contract_... | null | Program/logic/ContractsLogic.py | ContractsLogic.py | py | 1,620 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "data.dataAPI.dataAPI",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "models.contracts.Contract",
"line_number": 15,
"usage_type": "call"
}
] |
521184386 | import logging
import datetime
import os
import urllib2
from elasticsearch_dsl.query import Q, MultiMatch, Term
from designsafe.apps.data.models.elasticsearch import IndexedFile
#pylint: disable=invalid-name
logger = logging.getLogger(__name__)
#pylint: enable=invalid-name
class FileManager(object):
"""Elasticsea... | null | designsafe/apps/data/managers/elasticsearch.py | elasticsearch.py | py | 2,011 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "logging.getLogger",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "designsafe.apps.data.models.elasticsearch.IndexedFile.search",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "designsafe.apps.data.models.elasticsearch.IndexedFile",
"line_n... |
259500181 |
# 模板包 封装好的。不用分步骤去使用模板文件
# redirect 网页跳转包
# render 是渲染
# reverse 反向解析
from django.shortcuts import render, redirect, reverse
from django.http import HttpResponse, JsonResponse
# 导入加载模板文件包
from django.template import loader
# 导入定义模板上下文的包
from django.template import RequestContext
# 导入模型类包
from .models import BookInfo... | null | test1/booktest/views.py | views.py | py | 13,468 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "django.shortcuts.render",
"line_number": 62,
"usage_type": "call"
},
{
"api_name": "django.shortcuts.render",
"line_number": 68,
"usage_type": "call"
},
{
"api_name": "django.shortcuts.render",
"line_number": 73,
"usage_type": "call"
},
{
"api_name"... |
18155068 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sun Jul 9 14:24:52 2017
@author: mmrosek
"""
from skimage.filters import threshold_otsu, rank, threshold_local
try:
from skimage import filters
except ImportError:
from skimage import filter as filters
import matplotlib.pyplot as plt
import numpy a... | null | Connected Components Analysis/cca_4_5_llo_1_full_vid_seg_cells_updated_7_26.py | cca_4_5_llo_1_full_vid_seg_cells_updated_7_26.py | py | 6,972 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "matplotlib.backends.backend_pdf.PdfPages",
"line_number": 50,
"usage_type": "call"
},
{
"api_name": "matplotlib.backends.backend_pdf.PdfPages",
"line_number": 51,
"usage_type": "call"
},
{
"api_name": "matplotlib.backends.backend_pdf.PdfPages",
"line_number": 5... |
371101529 | #!/usr/bin/env python
from datetime import datetime
import os
import subprocess
import boto3
import yaml
TIMESTAMP = datetime.utcnow().isoformat().replace(':', '').replace('-', '')
SETTINGS = yaml.safe_load(open(os.path.expanduser("~/.hexagony.net.yaml")))
BUCKET = SETTINGS["bucket"]
CLOUDFRONT_DISTRIBUTION_ID = SETT... | null | scripts/deploy.py | deploy.py | py | 2,827 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "datetime.datetime.utcnow",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "datetime.datetime",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "yaml.safe_load",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "os.path.expan... |
105077345 |
import datetime
def dif_time():
time_1 = input("Input first date Ymd without spaces: ")
time_dt_1 = datetime.datetime.strptime(time_1, '%Y%m%d')
time_2 = input("Input second date Ymd without spaces: ")
time_dt_2 = datetime.datetime.strptime(time_2, '%Y%m%d')
delta = time_dt_2 - time_dt_1
days ... | null | Practice/a.isaev/Homework_lec_7/Homework_lection_7_task_1.py | Homework_lection_7_task_1.py | py | 696 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "datetime.datetime.strptime",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "datetime.datetime",
"line_number": 6,
"usage_type": "attribute"
},
{
"api_name": "datetime.datetime.strptime",
"line_number": 8,
"usage_type": "call"
},
{
"api_nam... |
165674999 | # coding=utf-8
# Q124:
# Write a function char_freq() that takes a string and builds a frequency listing of the characters
# contained in it.
# Represent the frequency listing as a Python dictionary.
# Try it with something like char_freq("abbabcbdbabdbdbabababcbcbab").
from collections import Counter
def char_freq(... | null | tasks/junior/answers/q124.py | q124.py | py | 977 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "collections.Counter",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "my_profiler.profiler.fprofile",
"line_number": 31,
"usage_type": "call"
},
{
"api_name": "my_profiler.profiler",
"line_number": 31,
"usage_type": "name"
}
] |
327034417 |
import scrapy
from lxml import etree
class Test(scrapy.Spider):
name = 'test1'
start_urls = ['https://www.julyedu.com/category/index']
def parse(self,response):
print("我是测试parse")
print (type(response))
#print (response.xpath('//div[@class="course_info_box"]/a/h4/text()'))
i... | null | 爬虫/scrapy_test/scrapy_spider_test.py | scrapy_spider_test.py | py | 911 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "scrapy.Spider",
"line_number": 4,
"usage_type": "attribute"
}
] |
507268339 | from typing import List, Optional, Union, TextIO
import pathlib
from more_itertools import first
from libs import us_state_abbrev
import pandas as pd
from libs.datasets.dataset_utils import AggregationLevel, make_rows_key
from libs.datasets import dataset_utils
from libs.datasets import custom_aggregations
from libs.... | null | libs/datasets/latest_values_dataset.py | latest_values_dataset.py | py | 6,934 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "libs.datasets.dataset_base.DatasetBase",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "libs.datasets.dataset_base",
"line_number": 17,
"usage_type": "name"
},
{
"api_name": "libs.datasets.common_fields.CommonIndexFields.AGGREGATE_LEVEL",
"line_... |
295217330 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals, print_function
import codecs
import logging
import sys
from copy import copy
from .grammar import EMPTY, STOP
from .tables import LALR, SLR, SHIFT, REDUCE, ACCEPT
from .exceptions import ParseError, ParserInitError, DisambiguationError, \
DynamicDisam... | null | parglare/parser.py | parser.py | py | 40,662 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sys.version",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "logging.getLogger",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "tables.LALR",
"line_number": 34,
"usage_type": "name"
},
{
"api_name": "grammar.EMPTY.action",... |
609559746 | #!/usr/bin/env python
import os
from setuptools import setup, find_packages
requirements = [
"dask-ms[xarray]",
"dask[complete]",
"datashader @ git+https://github.com/o-smirnov/datashader.git",
"holoviews",
"matplotlib>2.2.3; python_version >= '3.5'",
"cmasher",
"future-fstrings",
"requests",
"MSUtils"
]
extras_requ... | null | setup.py | setup.py | py | 1,265 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "setuptools.setup",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "setuptools.find_packages",
"line_number": 29,
"usage_type": "call"
},
{
"api_name": "os.listdir",
"line_number": 33,
"usage_type": "call"
}
] |
176141794 | from math import *
import numpy as np
from Distances import Distances
from NFWlens import NFWlens
from Sersic import Sersic
import matplotlib.pyplot as plt
from scipy.optimize import bisect
class lens_stat1D():
"""
=======================================================
Lens Parameters:
===============... | null | onedimLens.py | onedimLens.py | py | 5,853 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "Distances.Distances",
"line_number": 44,
"usage_type": "call"
},
{
"api_name": "NFWlens.NFWlens",
"line_number": 45,
"usage_type": "call"
},
{
"api_name": "Sersic.Sersic",
"line_number": 46,
"usage_type": "call"
},
{
"api_name": "numpy.atleast_1d",
... |
398527391 | import mysql.connector as mariadb
import rg_api_key
import requests
import json
import asyncio
import datetime
connection = mariadb.connect(user='root', password='123321Almitimo', database='RiotGames')
cursor = connection.cursor()
#Create Table in MySQL DB==========================================================... | null | lol_sql.py | lol_sql.py | py | 22,428 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "mysql.connector.connect",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "mysql.connector",
"line_number": 11,
"usage_type": "name"
},
{
"api_name": "requests.get",
"line_number": 75,
"usage_type": "call"
},
{
"api_name": "requests.get",
... |
483530729 | from django.urls import path
from . import views
# from dal import autocomplete
urlpatterns = [
path('list', views.StudentListView.as_view(), name='students'),
path('test', views.test, name='test'),
path('student/<int:pk>', views.StudentDetailView.as_view(), name='student'),
path('student2/<int:pri... | null | students/urls.py | urls.py | py | 1,120 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "django.urls.path",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "django.urls.path",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "django.urls.path",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "django.urls.path",... |
11068048 | #!/usr/bin/env python3
import argparse
from cyvcf2 import VCF
from pysam import FastaFile
def create_library(name, record, chrom, begin, end=None, flank_size=20):
""" Create a library line
record should be a pysam FastaFile
"""
if not end:
end = begin + 1
left_marker = record.fetch(re... | null | create-library.py | create-library.py | py | 1,878 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pysam.FastaFile",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "cyvcf2.VCF",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "argparse.ArgumentParser",
"line_number": 43,
"usage_type": "call"
},
{
"api_name": "argparse.ArgumentD... |
87192425 | # coding: utf-8
import csv
from pathlib import Path
#Initial loan cost list
loan_costs = [500, 600, 200, 1000, 450]
# How many loans are in the list?
# Print the number of loans from the list
number_of_loans = len(loan_costs)
print(f"A total of {number_of_loans} loans were issued.")
# What is the total of all loans... | null | Challenge_1/Challenge_1/loan_analyzer.py | loan_analyzer.py | py | 4,247 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pathlib.Path",
"line_number": 129,
"usage_type": "call"
},
{
"api_name": "csv.writer",
"line_number": 134,
"usage_type": "call"
}
] |
307686350 | #!/usr/bin/env python
# -*- coding:utf-8 -*-
# __author__ = "10291"
# 删除剪贴板文本中的换行符
import pyperclip
import time
a = 1
tt = pyperclip.paste()
print(pyperclip.paste())
copyBuff = ''
while True:
time.sleep(2)
CopiedText = pyperclip.paste()
if copyBuff != CopiedText:
copyBuff = CopiedText
no... | null | DelHuanhang.py | DelHuanhang.py | py | 529 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pyperclip.paste",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "pyperclip.paste",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "time.sleep",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "pyperclip.paste",
"lin... |
122392954 | """ I tried the naive make a choice, undo a choice method with a stack,
but time limit exceeded.
Use DP rather than recursion or backtracking. Memoize the all possible sentences
in a list.
After that, try optimization.
"""
from typing import List
class Solution:
def wordBreak(self, s: str, wor... | null | 140. Word Break II.py | 140. Word Break II.py | py | 1,116 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "typing.List",
"line_number": 10,
"usage_type": "name"
}
] |
30765453 | """myweb URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/3.0/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: path('', views.home, name='home')
Class-based v... | null | main/urls.py | urls.py | py | 2,627 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "django.urls.path",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "django.urls.path",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "django.urls.path",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "django.urls.path",... |
362305116 | import os.path as pt
import matplotlib.pyplot as plt
import torch
from torch import autograd
from tqdm import tqdm
from sig_lib.ResFNN import ResFNN
from sig_lib.sig_conditional import get_dataset, supervised_losses, to_numpy, compare_cross_corr
from sig_lib.sig_conditional import sample
from sig_lib.tools import sam... | null | sig_lib/baselines.py | baselines.py | py | 8,705 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sig_lib.sig_conditional.get_dataset",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "sig_lib.ResFNN.ResFNN",
"line_number": 41,
"usage_type": "call"
},
{
"api_name": "torch.optim.Adam",
"line_number": 42,
"usage_type": "call"
},
{
"api_na... |
506301692 | import numpy as np
import os
from skimage import img_as_float
from skimage.io import imread
from glob import glob
from PIL import Image
def load_imgset(path, load_masks=False, C=65535.):
lr_paths = glob(os.path.join(path,'LR*.png'))
lrs = np.array([np.array(Image.open(i)) / C for i in lr_paths])
hr = np.ar... | null | trainer/utils.py | utils.py | py | 1,346 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "glob.glob",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 9,
"usage_type": "attribute"
},
{
"api_name": "numpy.array",
"line_number": 10,... |
606516158 | #!/usr/bin/env python3.6
import json
import os
from pprint import pprint
from subprocess import check_output, CalledProcessError
REGION = "us-east-1"
def aws(*args, ignore_errors=False):
cmd = ['aws', '--region', REGION, '--output', 'json'] + list(args)
print('+ ' + ' '.join(cmd))
try:
output = ... | null | jobs/integration/tigera/cleanup_vpcs.py | cleanup_vpcs.py | py | 2,131 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "subprocess.check_output",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "subprocess.CalledProcessError",
"line_number": 16,
"usage_type": "name"
},
{
"api_name": "json.loads",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "ppri... |
416648058 | """Taxonomy, naming categories."""
from neomodel import (
ArrayProperty as ListProp,
StructuredNode as Model,
StringProperty as StringProp,
UniqueIdProperty,
RelationshipTo,
RelationshipFrom,
)
from plantstuff.schema_graph.formatters import basic_choice
PLANT_CATEGORY = [
"angiosperm",
... | null | plantstuff/schema_graph/models/taxonomy.py | taxonomy.py | py | 29,709 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "neomodel.StructuredNode",
"line_number": 1668,
"usage_type": "name"
},
{
"api_name": "neomodel.StringProperty",
"line_number": 1671,
"usage_type": "call"
},
{
"api_name": "plantstuff.schema_graph.formatters.basic_choice",
"line_number": 1671,
"usage_type": ... |
187643153 | import logging
from abc import ABC, abstractmethod
import requests
logger = logging.getLogger(__name__)
class CannotGetTokenPriceFromApi(Exception):
pass
class PriceOracle(ABC):
@abstractmethod
def get_price(self, ticker) -> float:
pass
class Binance:
"""
Get valid symbols from https... | null | safe_relay_service/tokens/exchanges.py | exchanges.py | py | 2,954 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "logging.getLogger",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "abc.ABC",
"line_number": 13,
"usage_type": "name"
},
{
"api_name": "abc.abstractmethod",
"line_number": 14,
"usage_type": "name"
},
{
"api_name": "requests.get",
"line_... |
16368025 | #!/usr/bin/python
# -*- coding: utf-8 -*-
from lettuce import step, before, after
from selenium.webdriver import Firefox
from selenium.webdriver.common.keys import Keys
from selenium.webdriver.common.by import By
from selenium.webdriver.support.ui import Select, WebDriverWait
from selenium.webdriver.support import expe... | null | testcode.py | testcode.py | py | 5,095 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "selenium.webdriver.Firefox",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "selenium.webdriver.support.ui.WebDriverWait",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "lettuce.before.all",
"line_number": 20,
"usage_type": "attribute"
... |
476537297 | #!/usr/bin/python3
from __future__ import print_function
from base64 import b64encode
from time import sleep, time, strftime, gmtime
from fnmatch import fnmatch
import socket
import ssl
from random import *
import os
import sys
import threading
import hashlib
import urllib.request
startTime = time()
messagesSeen = 0
... | null | main.py | main.py | py | 33,657 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "time.time",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "os.path.exists",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 24,
"usage_type": "attribute"
},
{
"api_name": "os.mkfifo",
"line_number": ... |
345581548 | import nltk
import random
import re
from statistics import mode
from nltk.corpus import movie_reviews
from nltk.classify.scikitlearn import SklearnClassifier
from nltk.classify import ClassifierI
from nltk.tokenize import word_tokenize
from sklearn.naive_bayes import MultinomialNB, BernoulliNB
from sklearn.linear_model... | null | sent_mod_train.py | sent_mod_train.py | py | 5,219 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "nltk.classify.ClassifierI",
"line_number": 15,
"usage_type": "name"
},
{
"api_name": "statistics.mode",
"line_number": 24,
"usage_type": "call"
},
{
"api_name": "statistics.mode",
"line_number": 31,
"usage_type": "call"
},
{
"api_name": "pickle.dump... |
653648248 | import requests
import time
import math
import numpy
import os
import datetime
import logging
import sys
import traceback
import argparse
import json
import paho.mqtt.client as mqtt
appName = "netatmo2graphite"
try:
from systemd.journal import JournalHandler
logger = logging.getLogger(appName)
logger.addH... | null | netatmo.py | netatmo.py | py | 10,596 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "logging.getLogger",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "systemd.journal.JournalHandler",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "logging.getLogger",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "lo... |
86007937 | """
Module for parsing FUMBBL API XML match reports.
"""
def match_dict_iter(data):
"""
Yields Python dictionaries of matches of data.
:param data: getter data
:type data: bytes
:yield: match data dictionary
:ytype: dict
"""
match_ets = match_et_iter(data)
yield from (Parser(match_et)() for match_et... | null | formats/xml_matches/parse.py | parse.py | py | 8,854 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "xml.etree.ElementTree.fromstring",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "xml.etree.ElementTree",
"line_number": 28,
"usage_type": "name"
}
] |
77996709 | import pandas as pd
import numpy as np
import pickle
from us import states
def covidtracking_ustates():
## ========== Import
# import covidtracking data
# see: data/covidtracking_update.py
with open("../data/covidtracking/covidtracking_dfs.pickle", "rb") as file:
covidtracking_dfs = pickle.loa... | null | src/localutils/dataload.py | dataload.py | py | 1,359 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pickle.load",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "pandas.read_csv",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "pandas.to_datetime",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "us.states.lookup",
... |
587734848 | #!/usr/bin/python
# Classification (U)
"""Program: run_program.py
Description: Unit testing of run_program in mail_2_rmq.py.
Usage:
test/unit/mail_2_rmq/run_program.py
Arguments:
"""
# Libraries and Global Variables
# Standard
import sys
import os
if sys.version_info < (2, 7):
import u... | null | test/unit/mail_2_rmq/run_program.py | run_program.py | py | 7,791 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sys.version_info",
"line_number": 21,
"usage_type": "attribute"
},
{
"api_name": "sys.path.append",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": "sys.path",
"line_number": 30,
"usage_type": "attribute"
},
{
"api_name": "os.getcwd",
"... |
559591669 | # ----------------------------------------------------------------------------#
# Imports
# ----------------------------------------------------------------------------#
import os, sys, datetime
import json
import dateutil.parser
import babel
from flask import Flask, render_template, request, Response, flash, redirect... | null | projects/01_fyyur/starter_code/app.py | app.py | py | 18,314 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "flask.Flask",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "flask_moment.Moment",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "flask_sqlalchemy.SQLAlchemy",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "flask_mig... |
9244093 | from boto3 import resource
from app.podcasts import ep_num_file
import re
s3 = resource('s3')
bucket = s3.Bucket('pitpodcast')
def podcast_title(title):
"""Converts the filename to an appropriate title"""
title = re.sub(r'Released/', r'', title)
title = title.rstrip('.mp3')
title = re.sub(r'^(ep)*(pi... | null | app/aws.py | aws.py | py | 1,141 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "boto3.resource",
"line_number": 5,
"usage_type": "call"
},
{
"api_name": "re.sub",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "re.sub",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "re.I",
"line_number": 13,
"usage_... |
469654674 | import paddle
import argparse
import numpy as np
import paddle.fluid as fluid
def parse_args():
parser = argparse.ArgumentParser(description='paddle activation op test')
parser.add_argument("op", type=str, choices=['leaky_relu'], help="activation op in 'leaky_relu', default is 'leaky_relu'", default='leaky_relu')
... | null | paddle/op/fluid_activation.py | fluid_activation.py | py | 975 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "argparse.ArgumentParser",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "paddle.fluid.layers.data",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "paddle.fluid.layers",
"line_number": 13,
"usage_type": "attribute"
},
{
"api_name... |
163889258 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
import glob
import os
import pickle
import numpy as np
from sklearn.feature_extraction.text import CountVectorizer, HashingVectorizer, TfidfTransformer
from aidistillery.file_handling import identifier_from_path
def tfidf_main(args):
print('Beginning tfidf_main().... | null | aidistillery/index_tfidf.py | index_tfidf.py | py | 2,287 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "glob.glob",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 16,
"usage_type": "attribute"
},
{
"api_name": "sklearn.feature_extraction.text.H... |
511854759 | #Device Detector to detect when the specific phone is present
#Author: Joshua Roberts
import nmap
import Constants
networkScanner = nmap.PortScanner()
device = Constants.LOCAL_NETWORK_PORT
def detectSpecificDevice():
"Detect whether a certain device is nearby"
networkScanner.scan(device)
for detectedDevic... | null | DeviceDetector.py | DeviceDetector.py | py | 446 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "nmap.PortScanner",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "Constants.LOCAL_NETWORK_PORT",
"line_number": 7,
"usage_type": "attribute"
}
] |
332565940 | import re
import datetime
from utils import Parser, EasySource, Source
class Canteen(EasySource):
def __init__(self, *args, location, needed_title, meta):
super(Canteen, self).__init__(*args)
self.location = location
self.needed_title = needed_title
self.meta = meta
def parse... | null | parsers/halle.py | halle.py | py | 4,875 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "utils.EasySource",
"line_number": 7,
"usage_type": "name"
},
{
"api_name": "datetime.datetime.now",
"line_number": 67,
"usage_type": "call"
},
{
"api_name": "datetime.datetime",
"line_number": 67,
"usage_type": "attribute"
},
{
"api_name": "utils.So... |
104061956 | import numpy as np
import cv2
img = cv2.imread("../input/img4.jpg", 0)
cv2.imshow("image", img)
new_img = np.zeros([img.shape[0], img.shape[1]], dtype="uint8")
for i in range(img.shape[0]):
for j in range(img.shape[1]):
new_img[i, j] = ((np.floor(img[i, j]/255 * 8 ) / 8)* 255).astype("uint8")
cv... | null | Practice questions/scripts/q5.py | q5.py | py | 367 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "cv2.imread",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "cv2.imshow",
"line_number": 6,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "numpy.floor",
"line_number": 11,
... |
489870694 | from django.contrib import admin
from .models import SanPham, SimNamSinh, SimTheoGia, SimTheoLoai, NhaMang
from import_export import resources, fields, widgets
from import_export.admin import ImportExportModelAdmin, ExportActionMixin
# Register your models here.
################--Xuất nhập khẩu--###################... | null | PythonWeb/sanpham/admin.py | admin.py | py | 6,002 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "import_export.resources.ModelResource",
"line_number": 12,
"usage_type": "attribute"
},
{
"api_name": "import_export.resources",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "models.SanPham",
"line_number": 16,
"usage_type": "name"
},
{
... |
109498405 | import logging
from db.dbclient import MongoClient
import feedparser
from bs4 import BeautifulSoup
import requests as req
from pymongo import ReplaceOne
from tqdm import tqdm
from ml.review_model import ReviewModel
class Feed:
# type, attr, value
meta_keywords = [
('meta_keywords','name','news_keywords... | null | src/etl/rss.py | rss.py | py | 6,284 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "db.dbclient.MongoClient",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "db.dbclient.MongoClient",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "feedparser.parse",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "logg... |
119674158 | # -*- coding:utf-8 -*-
# ! python3
'''
Copyright 2017 Sebastian Bauer
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 require... | null | ThargoidHunt/insert_systems.py | insert_systems.py | py | 5,611 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "ThargoidHunt.EliteSystem",
"line_number": 35,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 36,
"usage_type": "attribute"
},
{
"api_name": "os.getcwd",
"l... |
492278154 | import pandas as pd
from typing import List
import movielens as ml
from recommender import Recommender
def ask_user_id(user_list: List[int]) -> int:
"""
read user_id from input. must be integer and be contained in the user_list.
:param user_list: list of permitted user_ids.
:return: a valid user_id
... | null | assignment4/main.py | main.py | py | 1,763 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "typing.List",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "pandas.merge",
"line_number": 33,
"usage_type": "call"
},
{
"api_name": "movielens.rating",
"line_number": 33,
"usage_type": "attribute"
},
{
"api_name": "movielens.rating",
... |
242250710 | import pandas as pd
#from IPython.display import Markdown, display, clear_output
import os
import gensim
from gensim.test.utils import datapath, get_tmpfile
from gensim.models import KeyedVectors
import _pickle as cPickle
from pathlib import Path
import spacy
import logging
from spacy import displacy
nlp = spacy.load('... | null | Final.py | Final.py | py | 9,428 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "spacy.load",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "_pickle.dump",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "_pickle.load",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "pathlib.Path",
"line_number"... |
91374572 | import sys, os, csv, django
dir_path = os.path.dirname(os.path.realpath(__file__))
dir_path = os.path.dirname(dir_path)
sys.path.append(dir_path)
os.environ['DJANGO_SETTINGS_MODULE'] = 'Bookstore.settings'
django.setup()
from database.models import *
data = csv.reader(open("ADDRESS.csv"),delimiter=",")
firstline = ... | null | Bookstore/sample_data/readAddresses.py | readAddresses.py | py | 992 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "os.path.dirname",
"line_number": 3,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 3,
"usage_type": "attribute"
},
{
"api_name": "os.path.realpath",
"line_number": 3,
"usage_type": "call"
},
{
"api_name": "os.path.dirname",
"lin... |
337129146 | import pandas as pd
from calendar import monthrange
from dateutil.relativedelta import relativedelta
from pywr.parameters import Parameter
from utilities.converter import convert
import random
class FlowPeriods(object):
DRY_SEASON = 'dry season'
FALL_PULSE = 'fall pulse'
WET_SEASON = 'wet season'
SPRI... | null | pywr_models/parameters/__init__.py | __init__.py | py | 20,411 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pywr.parameters.Parameter",
"line_number": 23,
"usage_type": "name"
},
{
"api_name": "dateutil.relativedelta.relativedelta",
"line_number": 82,
"usage_type": "call"
},
{
"api_name": "calendar.monthrange",
"line_number": 93,
"usage_type": "call"
},
{
... |
163821031 | import numpy as np
import scipy.signal as s
import matplotlib.pyplot as plt
import pandas as pd
import scipy.signal as sig
import src.models.analytical_sdof_model as an_sdof
import scipy.interpolate as interp
class Dataset_Plotting(object):
"""This function generates plots from certain attributes that a dataset m... | null | src/features/proc_lib.py | proc_lib.py | py | 45,724 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "matplotlib.pyplot.figure",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 19,
"usage_type": "name"
},
{
"api_name": "matplotlib.pyplot.vlines",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "m... |
379069851 | from config.dbconfig import pg_config
import psycopg2
class CompanyDAO:
def __init__(self):
connection_url = "dbname=%s user=%s password=%s" % (pg_config['dbname'],
pg_config['user'],
p... | null | dao/company.py | company.py | py | 3,513 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "config.dbconfig.pg_config",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "config.dbconfig.pg_config",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "config.dbconfig.pg_config",
"line_number": 10,
"usage_type": "name"
},
{
"api_n... |
137621428 | import numpy
import scipy.constants as constants
import at
import math
import warnings
__all__ = ['find_orbit4', 'find_sync_orbit', 'find_orbit6', 'find_m44', 'find_m66', 'get_twiss']
XYDEFSTEP = 6.055454452393343e-006 # Optimal delta?
DPSTEP = 6.055454452393343e-006 # Optimal delta?
DDP = 1e-8
STEP_SIZE = 1e-6
MAX... | null | pyat/at/physics.py | physics.py | py | 20,646 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.uint32",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "numpy.float64",
"line_number": 18,
"usage_type": "attribute"
},
{
"api_name": "numpy.float64",
"line_number": 19,
"usage_type": "attribute"
},
{
"api_name": "numpy.float64... |
635827058 | import pathlib
import numpy as np
from dambreak import Experiment2D
from plot import plot2D, plot3D, quiver
#%%
def h0_(x, y, x0, y0, h0, hd):
H = np.ones_like(x) * hd # 1e-16
idx = np.array((x <= x0) & (y <= y0))
H[idx] = h0
return H
#%%
h_0 = 40
h_d = 1
x0 = 1000
y0 = 1000
L = 2000
T = 40
Nx = 100
Ny = 1... | null | 2/src/3_2D_stoker.py | 3_2D_stoker.py | py | 3,177 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.ones_like",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "numpy.array",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "numpy.abs",
"line_number": 30,
"usage_type": "call"
},
{
"api_name": "dambreak.Experiment2D",
"lin... |
429036070 | import os
import matplotlib.colors as colours
from matplotlib import cm as colormap
from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
from matplotlib.figure import Figure
from astropy.modeling import models, fitting
from scipy.ndimage import binary_dilation
import numpy as np
from copy imp... | null | pocs/focuser/focuser.py | focuser.py | py | 22,690 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "copy.copy",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "matplotlib.cm.inferno",
"line_number": 22,
"usage_type": "attribute"
},
{
"api_name": "matplotlib.cm",
"line_number": 22,
"usage_type": "name"
},
{
"api_name": "pocs.base.PanBase"... |
619062947 | from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import tensorflow as tf
import sys
sys.path.append('./models/research/slim/')
from nets import nets_factory
from utils.custom_preprocessing import preprocessing_factory
from utils.dataset import GoogleDataset
... | null | CNNs/google-landmark/train.py | train.py | py | 9,301 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sys.path.append",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "sys.path",
"line_number": 8,
"usage_type": "attribute"
},
{
"api_name": "tensorflow.contrib",
"line_number": 13,
"usage_type": "attribute"
},
{
"api_name": "tensorflow.app.fl... |
461429857 | import cv2
import numpy as np
cap = cv2.VideoCapture(0)
def count(x):
x = x+1
cv2.namedWindow('tiltshift')
cv2.createTrackbar('Alfa', 'tiltshift', 0, 100, count)
cv2.createTrackbar('Center', 'tiltshift', 0, 100, count)
cv2.createTrackbar('Height', 'tiltshift', 0, 100, count)
bg = cv2.imread('./Imagens/ts.jpg')
... | null | tilt-shift-video.py | tilt-shift-video.py | py | 1,231 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "cv2.VideoCapture",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "cv2.namedWindow",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "cv2.createTrackbar",
"line_number": 11,
"usage_type": "call"
},
{
"api_name": "cv2.createTrackbar"... |
214808521 | import webapp2
from google.appengine.api import users
import jinja2
import os
JINJA_ENVIRONMENT = jinja2.Environment(
loader=jinja2.FileSystemLoader(os.path.join(os.path.dirname(__file__),'templates')))
class MainPage(webapp2.RequestHandler):
def get(self):
user = users.get_current_user()
... | null | homeauth_gae/index.py | index.py | py | 594 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "jinja2.Environment",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "jinja2.FileSystemLoader",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "os.path.join",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "os.path",
"l... |
520802701 | import numpy as np
import sqlite3
import numpy as np
import pickle
import re
def data_parser():
'''
针对每一个玩家的动作序列，制作相应的动作序列和标签
'''
conn = sqlite3.connect('./data/an.db2')
c = conn.cursor()
query_sql = "SELECT user_id, op, current_day, num_days_played, relative_timestamp \
FROM maidian O... | null | utils/pearson.py | pearson.py | py | 5,174 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sqlite3.connect",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "pickle.dump",
"line_number": 49,
"usage_type": "call"
},
{
"api_name": "pickle.dump",
"line_number": 50,
"usage_type": "call"
},
{
"api_name": "sqlite3.connect",
"line_n... |
166153612 | import numpy as np
import cv2
import matplotlib.pyplot as plt
class Matcher:
def __init__(self, keypoints_query, keypoints_test, lowe_tau=0.75, test_lowe=True, cross_check=True):
self.keypoints_query = keypoints_query
self.keypoints_test = keypoints_test
self.lowe_tau = lowe_tau
... | null | new_matching.py | new_matching.py | py | 2,762 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.count_nonzero",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot.cm.get_cmap",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot.cm",
"line_number": 26,
"usage_type": "attribute"
},
{
"api_n... |
105993892 | import random
import re
import time
import serial
import platform
from serial.tools import list_ports
from Utilities import replace_value_with_definition, readify_data, string_me
import datetime
CHANNEL_TYPE_UNKNOWN = 0
CHANNEL_TYPE_SENSOR = 1
CHANNEL_TYPE_IMU = 2
CHANNEL_TYPE_GPS = 3
CHANNEL_TYPE_TIME = 4
CHANNEL_TY... | null | Process.py | Process.py | py | 12,048 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "serial.Serial",
"line_number": 102,
"usage_type": "call"
},
{
"api_name": "time.time",
"line_number": 105,
"usage_type": "call"
},
{
"api_name": "serial.tools.list_ports.comports",
"line_number": 110,
"usage_type": "call"
},
{
"api_name": "serial.to... |
464576997 | import argparse, json
''' import a json file, in each document, look for a lat, lon field, take it and export as kml file with points '''
def find_point_data(document, lat_field, lon_field):
return (document[lat_field], document[lon_field])
def load_file(file_path):
return json.loads(open(file_path, 'r').rea... | null | demographics_bar.py | demographics_bar.py | py | 1,507 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "json.loads",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "argparse.ArgumentParser",
"line_number": 25,
"usage_type": "call"
}
] |
334026471 |
import math
import scipy.special
def TemplateIon(xvect,c0,c1,c2,offset,ampl) :
# convention: les c0/c1/c2 = parametres du fichier tmplt.txt
xx = [x-offset for x in xvect]
fprepulse=[0 for x in xx if x<0]
fpostpulse=[ ampl*((1+scipy.special.erf(x/c2))/2.0)*(math.exp(-x/c0)-c1) for x in xx if x>=0]
... | null | Code/Run308_WIMP_searches/Run308_Analyse_ERA/ERA_manipulations/ERA/python/TemplateFct.py | TemplateFct.py | py | 1,782 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "scipy.special.special.erf",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "scipy.special.special",
"line_number": 9,
"usage_type": "attribute"
},
{
"api_name": "scipy.special",
"line_number": 9,
"usage_type": "name"
},
{
"api_name": "math.... |
383611881 | #!/usr/bin/env python
import sys
import rospy
import cv2
from std_msgs.msg import String
from sensor_msgs.msg import Image
from cv_bridge import CvBridge, CvBridgeError
import numpy as np
class image_converter:
def __init__(self):
self.image_pub = rospy.Publisher("openCV_image", Image, queue_size=10)
... | null | src/bebop_fyp/scripts/image_converter.py | image_converter.py | py | 2,170 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "rospy.Publisher",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "sensor_msgs.msg.Image",
"line_number": 13,
"usage_type": "argument"
},
{
"api_name": "cv_bridge.CvBridge",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "rospy.Su... |
552336078 | from django.shortcuts import render
from django.http import HttpResponse
import json
from django.views.decorators.csrf import csrf_exempt
from chatterbot import ChatBot
import random
chatbot = ChatBot('Norman')
def home(request, template_name="home.html"):
context = {'title': 'RemindMe'}
return render(request, te... | null | mysite/remindme/views.py | views.py | py | 1,036 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "chatterbot.ChatBot",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "django.shortcuts.render",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 21,
"usage_type": "call"
},
{
"api_name": "random.choice",... |
324723144 | import numpy as np
import gym
import random
import tensorflow as tf
from collections import deque
import time
REPLAY_MEMORY = 128 # number of previous transitions to remember
BATCH_SIZE = 16 # size of minibatch
EPISODES = 3000
GAMMA = 0.99
EPSILON = 0.1
EPSILON_MIN = 0.01
EPSILON_DECAY = 0.995
LEARNING_RATE = 0.1
TIME... | null | com/alodokter/learn/frozenDQN_experience_replay.py | frozenDQN_experience_replay.py | py | 4,529 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "gym.make",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "tensorflow.reset_default_graph",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "tensorflow.placeholder",
"line_number": 27,
"usage_type": "call"
},
{
"api_name": "tensor... |
648962329 | from helper import HelperFunctions
from selenium.common.exceptions import NoSuchElementException
from driver_functions import DriverFunctions
import time
class Finder():
"""
Holds the collections of methods that finds element of the instagram posts using selenium's
webdriver's methods
"""
@... | null | instagram_scraping/element_finder.py | element_finder.py | py | 19,574 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "driver_functions.DriverFunctions._DriverFunctions__wait_for_element_to_appear",
"line_number": 26,
"usage_type": "call"
},
{
"api_name": "driver_functions.DriverFunctions",
"line_number": 26,
"usage_type": "name"
},
{
"api_name": "driver_functions.DriverFunctions._... |
441537118 | from sqlalchemy import inspect
from sqlalchemy.ext.declarative import declarative_base
Base = declarative_base()
#==========================================================================================#
from sqlalchemy import Column, String,Integer,Sequence
class django_migrations(Base):
__tablena... | null | SQLachemy/transaction-query-update.py | transaction-query-update.py | py | 2,138 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "sqlalchemy.ext.declarative.declarative_base",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "sqlalchemy.Column",
"line_number": 12,
"usage_type": "call"
},
{
"api_name": "sqlalchemy.Integer",
"line_number": 12,
"usage_type": "argument"
},
{
... |
74598225 | #import matplotlib
#matplotlib.use('tkAgg')
import matplotlib.pyplot as plt
#import pylab as plt
mySamples=[]
myLinear=[]
myQuadratic=[]
myCubic=[]
myExponential=[]
for i in range(0,30):
mySamples.append(i)
myLinear.append(i)
myQuadratic.append(i**2)
myCubic.append(i**3)
myExponential.append(i**4)... | null | Unit7_Visualization/pylab.py | pylab.py | py | 1,511 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "matplotlib.pyplot.figure",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "matplotlib.pyplot",
"line_number": 19,
"usage_type": "name"
},
{
"api_name": "matplotlib.pyplot.clf",
"line_number": 20,
"usage_type": "call"
},
{
"api_name": "matp... |
66962238 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Jun 25 19:23:01 2019
@author: xuwanqian
"""
from __future__ import absolute_import, division, print_function
import numpy as np
import tensorflow as tf
from tensorflow.keras import Model, layers
from loadBasicEnv import NAMEDICT, IMGSHAPE, NET_SAVE_NA... | null | vgg.py | vgg.py | py | 7,627 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "loadBasicEnv.NAMEDICT",
"line_number": 21,
"usage_type": "argument"
},
{
"api_name": "tensorflow.keras.Model",
"line_number": 30,
"usage_type": "name"
},
{
"api_name": "tensorflow.keras.layers.Conv2D",
"line_number": 35,
"usage_type": "call"
},
{
"a... |
99630156 | # goal of this code is to use OpenCV's Blob detector to detector the washers using an
# OpenCV's Canny edge detection filter (not color)
# it needs the video frame as an input
# outputs the x and y coordinate to the closest blob detected
# the blob parameters are needed to limit the things that are detected as a "blob"... | null | subpartcode/Camera Vision/objcenter.py | objcenter.py | py | 2,988 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "cv2.SimpleBlobDetector_Params",
"line_number": 19,
"usage_type": "call"
},
{
"api_name": "cv2.__version__.split",
"line_number": 47,
"usage_type": "call"
},
{
"api_name": "cv2.__version__",
"line_number": 47,
"usage_type": "attribute"
},
{
"api_name... |
158036634 | # coding: utf-8
# Licensed to the Apache Software Foundation (ASF) under one
# or more contributor license agreements. See the NOTICE file
# distributed with this work for additional information
# regarding copyright ownership. The ASF licenses this file
# to you under the Apache License, Version 2.0 (the
# "... | null | scripts/question_answering/data_process.py | data_process.py | py | 11,811 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "spacy.blank",
"line_number": 53,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_number": 88,
"usage_type": "call"
},
{
"api_name": "tqdm.tqdm",
"line_number": 89,
"usage_type": "call"
},
{
"api_name": "random.shuffle",
"line_number": 9... |
628185988 | #!/usr/bin/python
import json
import pprint
from utils import *
from db import *
import web
def city_search(req):
name = req.params.getall('name')[0]
cities = db_city_search(req, name)
return web.JSONResponse(cities)
def postcode_search(req):
name = req.params.getall('name')[0]
search_name = na... | null | src/py/cities.py | cities.py | py | 1,232 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "web.JSONResponse",
"line_number": 14,
"usage_type": "call"
},
{
"api_name": "web.JSONResponse",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 49,
"usage_type": "call"
},
{
"api_name": "web.JSONResponse",
"... |
30604497 | from models import Activator, ModelTools
import activator_extension
import json
def build_application(app):
app_dict = {
'id': app.id,
'name': app.name,
'env': app.env,
'status': app.status,
'description': app.description,
'resources': json.loads(app.resources or '[... | null | application_extension.py | application_extension.py | py | 598 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "json.loads",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "models.Activator.query.filter",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "models.Activator.query",
"line_number": 16,
"usage_type": "attribute"
},
{
"api_name": "... |
80872937 | import json,time
def sanitizar(cadena):
resultado = ""
diccionario= {"á":"a","é":"e","í":"i","ó":"o","ú":"u","Á":"A","É":"E","Í":"I","Ó":"o","Ú":"u","ñ":"n", " ": " "}
for letra in cadena:
lista_claves = diccionario.keys()
if letra in lista_claves:
resultado += diccionario[letra... | null | cgi-bin/datos.py | datos.py | py | 2,922 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "json.load",
"line_number": 24,
"usage_type": "call"
}
] |
267115620 | from django.shortcuts import render
from django.http import HttpResponse
import json
import smtplib
def index(request):
return render(request, 'index.html', {})
def message(request):
if request.method == 'POST':
msg = request.POST.get('msg')
formData= request.POST.get('formData')
data = json.loads(formData)... | null | aedig/views.py | views.py | py | 1,049 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "django.shortcuts.render",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "json.loads",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "smtplib.SMTP",
"line_number": 28,
"usage_type": "call"
},
{
"api_name": "django.http.HttpRespon... |
7870144 | import numpy as np
from numpy import ndarray
from NN.Dense import Dense
from NN.Linear import Linear
from NN.MeanSquaredError import MeanSquaredError
from NN.NeuralNetwork import NeuralNetwork
from NN.SGD import SGD
from NN.Sigmoid import Sigmoid
from sklearn.datasets import load_boston
from sklearn.preprocessing imp... | null | NN/RunTraining.py | RunTraining.py | py | 3,066 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.ndarray",
"line_number": 18,
"usage_type": "name"
},
{
"api_name": "numpy.mean",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "numpy.abs",
"line_number": 22,
"usage_type": "call"
},
{
"api_name": "numpy.ndarray",
"line_number":... |
187196598 | from PIL import Image
from lego_sorter_server.analysis.classification.toolkit.transformations.transformation import Transformation
class Simple(Transformation):
@staticmethod
def transform(img, desired_size=299):
old_size = img.size
ratio = float(desired_size) / max(old_size)
new_size... | null | lego_sorter_server/analysis/classification/toolkit/transformations/simple.py | simple.py | py | 627 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "lego_sorter_server.analysis.classification.toolkit.transformations.transformation.Transformation",
"line_number": 6,
"usage_type": "name"
},
{
"api_name": "PIL.Image.ANTIALIAS",
"line_number": 12,
"usage_type": "attribute"
},
{
"api_name": "PIL.Image",
"line_nu... |
315782361 | import scipy
import numpy as np
import matplotlib.pyplot as plt
import math
import time
import random
start = time.time()
################ OPPGAVE 1 ################
def makeGrid(N): # Lager en nxn matrise. N=lengde av polymer. n=N+2
grid = np.zeros((N + 2, N + 2)).astype(np.int16)
n = len(grid)
grid[in... | null | Prosjekt2/fullstendig/opg124.py | opg124.py | py | 7,834 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "time.time",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "numpy.zeros",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "numpy.int16",
"line_number": 13,
"usage_type": "attribute"
},
{
"api_name": "numpy.round",
"line_number"... |
358051295 | '''
Copyright 2021 OpenDILab. All Rights Reserved:
Description:
'''
from functools import partial
from easydict import EasyDict
from ding.envs import SyncSubprocessEnvManager
from ding.utils import set_pkg_seed
from ding.utils.default_helper import deep_merge_dicts
from core.envs import SimpleCarlaEnv, CarlaEnvWrapp... | null | demo/coil_demo/coil_eval.py | coil_eval.py | py | 3,154 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "easydict.EasyDict",
"line_number": 77,
"usage_type": "call"
},
{
"api_name": "core.envs.CarlaEnvWrapper",
"line_number": 83,
"usage_type": "call"
},
{
"api_name": "core.envs.SimpleCarlaEnv",
"line_number": 83,
"usage_type": "call"
},
{
"api_name": "... |
312770324 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Mar 31 16:45:27 2018
@author: astricot
"""
import numpy as np
from sklearn.cluster import KMeans
from sklearn import cluster,datasets
from sklearn.manifold import TSNE
def tSNE_Nu(file1,file2):
N = np.load(file1)
N_embed = TSNE(n_components=2,... | null | Recommendation-System/clustering.py | clustering.py | py | 1,281 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.load",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "sklearn.manifold.TSNE",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "numpy.save",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "numpy.load",
"line_nu... |
638037647 | ########
# Copyright (c) 2016 GigaSpaces Technologies Ltd. All rights reserved
#
# 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... | null | cloudify_cli/tests/commands/test_deployment_update.py | test_deployment_update.py | py | 6,272 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "cloudify_cli.tests.commands.test_cli_command.CliCommandTest",
"line_number": 28,
"usage_type": "name"
},
{
"api_name": "mock.MagicMock",
"line_number": 37,
"usage_type": "call"
},
{
"api_name": "mock.MagicMock",
"line_number": 39,
"usage_type": "call"
},
... |
139229922 | # Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 appli... | null | models/match/dssm/model.py | model.py | py | 3,938 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "paddlerec.core.model.ModelBase",
"line_number": 21,
"usage_type": "name"
},
{
"api_name": "paddlerec.core.model.ModelBase.__init__",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "paddlerec.core.model.ModelBase",
"line_number": 23,
"usage_type": ... |
589544617 | #!/usr/bin/env python
# coding: utf-8
from tkinter import *
import numpy as np
import matplotlib
matplotlib.use("TkAgg")
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.figure import Figure
from tkinter import filedialog
from keras.models import load_model
import h5py
import csv
class ... | null | gui.py | gui.py | py | 5,784 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "matplotlib.use",
"line_number": 7,
"usage_type": "call"
},
{
"api_name": "matplotlib.figure.Figure",
"line_number": 37,
"usage_type": "call"
},
{
"api_name": "matplotlib.backends.backend_tkagg.FigureCanvasTkAgg",
"line_number": 42,
"usage_type": "call"
},... |
100058546 | import argparse
import torch
import torch.nn as nn
import matplotlib.pyplot as plt
import torch.nn.functional as F
import numpy as np
from datasets.mnist import mnist
import os
from torchvision.utils import make_grid
def log_prior(x):
"""
Compute the elementwise log probability of a standard Gaussian, i.e.
... | null | assignment_3/templates/a3_nf_template.py | a3_nf_template.py | py | 10,263 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "numpy.log",
"line_number": 17,
"usage_type": "call"
},
{
"api_name": "numpy.pi",
"line_number": 17,
"usage_type": "attribute"
},
{
"api_name": "torch.diag",
"line_number": 18,
"usage_type": "call"
},
{
"api_name": "torch.normal",
"line_number": ... |
28391689 | from django import forms
from fas_questionnaire.forms.common import ListTextWidget
from ..models.page5 import *
class CroppingPatternAndCropScheduleForm(forms.ModelForm):
class Meta:
model = CroppingPatternAndCropSchedule
fields = ['household',
'crop_number_first_digit',
... | null | fas_questionnaire_site/fas_questionnaire/forms/page5.py | page5.py | py | 2,222 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "django.forms.ModelForm",
"line_number": 7,
"usage_type": "attribute"
},
{
"api_name": "django.forms",
"line_number": 7,
"usage_type": "name"
},
{
"api_name": "fas_questionnaire.forms.common.ListTextWidget",
"line_number": 34,
"usage_type": "call"
},
{
... |
408817433 | import os
os.environ['CUDA_VISIBLE_DEVICES'] = '2' # change GPU here
import sys
sys.path.extend(['/home/data2/rzf/KD'])
import random
import numpy as np
import torch
import torch.backends.cudnn as cudnn
import math
import torch.nn.functional as F
import argparse
from PIL import Image
import matplotlib.py... | null | rzf_code_logs/907/train_sigma_stu_fc1.py | train_sigma_stu_fc1.py | py | 23,140 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "os.environ",
"line_number": 2,
"usage_type": "attribute"
},
{
"api_name": "sys.path.extend",
"line_number": 4,
"usage_type": "call"
},
{
"api_name": "sys.path",
"line_number": 4,
"usage_type": "attribute"
},
{
"api_name": "argparse.ArgumentParser",
... |
15880518 | from flask import session, url_for
from app import app, db
import os
from requests_oauthlib import OAuth2Session
from datetime import datetime
import time
tablename_prefix = os.path.dirname(os.path.realpath(__file__)).split("/")[-1]
class Base(db.Model):
__abstract__ = True
id = db.Column(db.Integer, primary... | null | app/authorize_qbo_blueprint/models.py | models.py | py | 3,722 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "os.path.dirname",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "os.path",
"line_number": 8,
"usage_type": "attribute"
},
{
"api_name": "os.path.realpath",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "app.db.Model",
"line_n... |
61365081 | from datetime import datetime
import twitter
from settings import Settings
from db import JSON
class TwitterUtils:
def __init__(self):
self.config = Settings()
self.settings = self.config.load()
self.jsonDB = JSON()
self.load()
self.api = twitter.Api(
consume... | null | twitterUtils.py | twitterUtils.py | py | 4,064 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "settings.Settings",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "db.JSON",
"line_number": 13,
"usage_type": "call"
},
{
"api_name": "twitter.Api",
"line_number": 16,
"usage_type": "call"
},
{
"api_name": "datetime.datetime.now",
"li... |
351830027 | # from pyecharts import Map
#
# value = [155,10,66,78,33,80,190,53,49.6]
# attr = ['福建','山东','北京','甘肃','新疆','河南','广西','西藏']
# map = Map("Test", width=1200, height=600)
# map.add("",attr,value,maptype='china',is_visualmap=True,visual_text_color='#000')
# map.show_config()
# map.render()
# from pyecharts import Map
#
#... | null | Test_pyecharts.py | Test_pyecharts.py | py | 2,361 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pyecharts.Map",
"line_number": 56,
"usage_type": "call"
}
] |
259213496 | # This file is part of DEAP.
#
# DEAP is free software: you can redistribute it and/or modify
# it under the terms of the GNU Lesser General Public License as
# published by the Free Software Foundation, either version 3 of
# the License, or (at your option) any later version.
#
# DEAP is distributed ... | null | 000_DOC/scripts_curso_ice/singlemax2.py | singlemax2.py | py | 3,835 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "deap.creator.create",
"line_number": 23,
"usage_type": "call"
},
{
"api_name": "deap.creator",
"line_number": 23,
"usage_type": "name"
},
{
"api_name": "deap.base.Fitness",
"line_number": 23,
"usage_type": "attribute"
},
{
"api_name": "deap.base",
... |
498415362 | import lib.alarm as alarm
import lib.client as client
import lib.peripherals as peripherals
import lib.SoftwareTimer as SoftwareTimer
import time
REFRESH_RATE = 5
myClock = alarm.AlarmClock()
client = client.Client()
while not client.connect("192.168.2.3", 12345): time.sleep(REFRESH_RATE)
refreshTime... | null | main.py | main.py | py | 1,176 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "lib.alarm.AlarmClock",
"line_number": 10,
"usage_type": "call"
},
{
"api_name": "lib.alarm",
"line_number": 10,
"usage_type": "name"
},
{
"api_name": "lib.client",
"line_number": 12,
"usage_type": "name"
},
{
"api_name": "lib.client.Client",
"li... |
179053761 | """
APIs for storing and retrieving HTTP headers and cookies.
"""
from bisect import insort
from cached_property import cached_property as calculated_once
from characteristic import Attribute, attributes
from future.moves.urllib.parse import (
parse_qs, unquote, unquote_plus, urlencode,
)
from future.utils impor... | null | minion/http.py | http.py | py | 13,536 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "future.utils.raise_with_traceback",
"line_number": 102,
"usage_type": "call"
},
{
"api_name": "future.moves.urllib.parse.unquote",
"line_number": 123,
"usage_type": "call"
},
{
"api_name": "future.moves.urllib.parse.unquote",
"line_number": 136,
"usage_type... |
602321713 | import csv
import pymongo
import json
import urllib.request
import time
from datetime import datetime, timedelta
conn = pymongo.MongoClient('127.0.0.1', 27017)
db = conn.get_database('scsc')
weather_col = db.get_collection('weather')
score_col = db.get_collection('score')
with open("../config/key.json", "r") as sk_j... | null | src/server/batch/shortRefresh.py | shortRefresh.py | py | 5,948 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "pymongo.MongoClient",
"line_number": 9,
"usage_type": "call"
},
{
"api_name": "json.load",
"line_number": 15,
"usage_type": "call"
},
{
"api_name": "csv.reader",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "datetime.datetime.now",
"... |
589424042 | #!/usr/bin/python
# Copyright 2012 Kuno Woudt
# All software related to the License Database project is licensed under
# the Apache License, Version 2.0. See the file Apache-2.0.txt for more
# information.
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distr... | null | src/build/turtle-cc.py | turtle-cc.py | py | 6,598 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "license.activate_virtualenv",
"line_number": 25,
"usage_type": "call"
},
{
"api_name": "codecs.open",
"line_number": 36,
"usage_type": "call"
},
{
"api_name": "rdflib.Graph",
"line_number": 44,
"usage_type": "call"
},
{
"api_name": "rdflib.term.URIR... |
144310093 | import re
import urllib.request, urllib.parse, urllib.error
from bs4 import BeautifulSoup
import urllib.parse
values={'q':'saranath history'}
data=urllib.parse.urlencode(values)
url='https://www.google.com/search?'+data
headers={}
headers['User-Agent']="Mozilla/5.0 (X11; Linux i686)"
request=urllib.r... | null | 1.py | 1.py | py | 1,552 | python | en | code | null | code-starcoder2 | 83 | [
{
"api_name": "urllib.request.parse.urlencode",
"line_number": 8,
"usage_type": "call"
},
{
"api_name": "urllib.request.parse",
"line_number": 8,
"usage_type": "attribute"
},
{
"api_name": "urllib.request",
"line_number": 8,
"usage_type": "name"
},
{
"api_name": "... |
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