text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|> style = None
if ref is not None:
try:
cell = self.oldsheet.cell(ref, c)
style = self.book.style_list[cell.xf_index]
except:
#print 'fallback failed'
style = None
if not style:
try:... | code_fim | hard | {
"lang": "python",
"repo": "shaung/xlpy",
"path": "/xlpy/xlutils/cne.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mators11/SudokuSolver path: /sudokusolver/common.py
#!/usr/bin/env python
# encoding: utf-8
def from_vals(vals):
if not vals: #Unsolved
return False
sudoku = []
for i in range(9):
line = []
for j in range(9):
line.append(int(vals[str(chr(ord('A') ... | code_fim | hard | {
"lang": "python",
"repo": "mators11/SudokuSolver",
"path": "/sudokusolver/common.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return to_grid(from_vals(vals))
def to_str_vals(vals):
return to_str(from_vals(vals))
def to_str(sudoku):
if sudoku == False:
return 'Unsolved'
out = ''
for i in range(9):
for j in range(9):
out = out + str(sudoku[i][j])
out = out + ' '
... | code_fim | hard | {
"lang": "python",
"repo": "mators11/SudokuSolver",
"path": "/sudokusolver/common.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """CText_Importer.import_from_string."""
c = self.c
root = parent.copy()
ft = c.importCommands.fileType.lower()
cchar = (
'#' if g.unitTesting else
'%' if ft == '.sql' else
'-' if ft == '.sql' else
'/' if ft == '.js' e... | code_fim | hard | {
"lang": "python",
"repo": "leo-editor/leo-editor",
"path": "/leo/plugins/importers/ctext.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: leo-editor/leo-editor path: /leo/plugins/importers/ctext.py
#@+leo-ver=5-thin
#@+node:tbrown.20140801105909.47549: * @file ../plugins/importers/ctext.py
from __future__ import annotations
import re
from typing import TYPE_CHECKING
from leo.core import leoGlobals as g # Required
from leo.plugins.... | code_fim | hard | {
"lang": "python",
"repo": "leo-editor/leo-editor",
"path": "/leo/plugins/importers/ctext.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> This would be the text in this level one node.
And this.
### Another level one node ###############################
Another one
#### A level 2 node ######################################
See what we did there - one more '#' - this is a subnode.
Lea... | code_fim | hard | {
"lang": "python",
"repo": "leo-editor/leo-editor",
"path": "/leo/plugins/importers/ctext.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: li5ch/Python-Notes path: /gevent_notes/gevent_tests.py
import time
import threading
from threading import Thread, Event
<|fim_suffix|>event = Event()
t = Thread(target=worker, args=(event,))
t.start()<|fim_middle|>def worker(event_obj):
i=3
while i:
localtime = time.asctime(time... | code_fim | hard | {
"lang": "python",
"repo": "li5ch/Python-Notes",
"path": "/gevent_notes/gevent_tests.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>event = Event()
t = Thread(target=worker, args=(event,))
t.start()<|fim_prefix|># repo: li5ch/Python-Notes path: /gevent_notes/gevent_tests.py
import time
import threading
from threading import Thread, Event
def worker(event_obj):
<|fim_middle|> i=3
while i:
localtime = time.asctime(time... | code_fim | hard | {
"lang": "python",
"repo": "li5ch/Python-Notes",
"path": "/gevent_notes/gevent_tests.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> tcp_flags = TCPControlBits(['SYN', 'ACK'])
assert_is_instance(tcp_flags.to_str(), str)
def test_should_return_a_integer(self):
""" Should return an int from the informed tcp flags """
tcp_flags = TCPControlBits(['SYN', 'ACK'])
assert_is_instance(tcp_flags.to_i... | code_fim | hard | {
"lang": "python",
"repo": "globocom/GloboNetworkAPI",
"path": "/networkapi/plugins/SDN/ODL/tests/test_tcp_control_bits.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Should return a hex from the informed tcp flags """
tcp_flags = TCPControlBits(['SYN', 'ACK'])
assert_is_instance(tcp_flags.to_hex(), str)
def test_should_return_the_correct_hexadecimal(self):
""" Should return the correct Hexadecimal from tcp flags """
t... | code_fim | hard | {
"lang": "python",
"repo": "globocom/GloboNetworkAPI",
"path": "/networkapi/plugins/SDN/ODL/tests/test_tcp_control_bits.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: globocom/GloboNetworkAPI path: /networkapi/plugins/SDN/ODL/tests/test_tcp_control_bits.py
# -*- coding: utf-8 -*-
from nose.tools import assert_raises
from nose.tools import assert_equal
from nose.tools import assert_false
from nose.tools import assert_is_instance
from networkapi.test.test_case... | code_fim | hard | {
"lang": "python",
"repo": "globocom/GloboNetworkAPI",
"path": "/networkapi/plugins/SDN/ODL/tests/test_tcp_control_bits.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> url = 'https://www.lagou.com/jobs/positionAjax.json?city=%E6%B7%B1%E5%9C%B3&needAddtionalResult=false'
# 先设定获取页数为1，获取总的职位数
page_1 = get_json(url, 1)
total_count = page_1['content']['positionResult']['totalCount']
num = get_page_num(total_count)
total_info = []
time.sleep(5)
... | code_fim | hard | {
"lang": "python",
"repo": "MisterZhouZhou/python3demo",
"path": "/demo/lagou.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def main():
url = 'https://www.lagou.com/jobs/positionAjax.json?city=%E6%B7%B1%E5%9C%B3&needAddtionalResult=false'
# 先设定获取页数为1，获取总的职位数
page_1 = get_json(url, 1)
total_count = page_1['content']['positionResult']['totalCount']
num = get_page_num(total_count)
total_info = []
time.... | code_fim | hard | {
"lang": "python",
"repo": "MisterZhouZhou/python3demo",
"path": "/demo/lagou.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MisterZhouZhou/python3demo path: /demo/lagou.py
import requests
import math
import time
import pandas as pd
def get_json(url,num):
'''''从网页获取JSON,使用POST请求,加上头部信息'''
my_headers = {
'User-Agent': 'Mozilla/5.0 (Windows NT 10.0; Win64; x64) AppleWebKit/537.36 (KHTML, like Gecko) Chro... | code_fim | hard | {
"lang": "python",
"repo": "MisterZhouZhou/python3demo",
"path": "/demo/lagou.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>
setup(
name='logging2telegram',
version='1.0.4',
packages=['log2tg'],
url='https://github.com/tezmen/loging2telegram',
author='tezmen',
license='Apache License, Version 2.0, see LICENSE file',
description='Telegram logging handler',
long_description=long_description(),
long_description_content_t... | code_fim | hard | {
"lang": "python",
"repo": "tezmen/logging2telegram",
"path": "/setup.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tezmen/logging2telegram path: /setup.py
# -*- coding: utf-8 -*-
from os import path
from setuptools import setup
def long_description():
this_dir = path.abspath(path.dirname(__file__))
with open(path.join(this_dir, 'README.md'), encoding='utf-8') as f:
return f.read()
<|fim_suffix|>setup(... | code_fim | hard | {
"lang": "python",
"repo": "tezmen/logging2telegram",
"path": "/setup.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hsingh23/courseflow path: /course/utils.py
# -*- coding: utf-8 -*-
from __future__ import division
__copyright__ = "Copyright (C) 2014 Andreas Kloeckner"
__license__ = """
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation... | code_fim | hard | {
"lang": "python",
"repo": "hsingh23/courseflow",
"path": "/course/utils.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> from course.content import get_flow_page_desc
self.page_desc = get_flow_page_desc(
flow_session, self.flow_desc, page_data.group_id, page_data.page_id)
self.page = instantiate_flow_page_with_ctx(self, page_data)
from course.page import PageContext
... | code_fim | hard | {
"lang": "python",
"repo": "hsingh23/courseflow",
"path": "/course/utils.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amoghkokari/weighted path: /weighted.py
def enmal():
adj=[]
mat={}
nod=[]
stop = False
print()
no = int(input("Enter the number of vertices in the graph : "))
print()
for i in range(no):
print("Enter",i+1,"node ",e... | code_fim | hard | {
"lang": "python",
"repo": "amoghkokari/weighted",
"path": "/weighted.py",
"mode": "psm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|> done = False
while not done:
try:
print()
print()
print("1.Enter value manually ")
print("2.Choose from the file ")
print("3.Exit")
print()
ch = int(input("Enter your choice(1-3) ... | code_fim | hard | {
"lang": "python",
"repo": "amoghkokari/weighted",
"path": "/weighted.py",
"mode": "spm",
"license": "Unlicense",
"source": "the-stack-v2"
} |
<|fim_suffix|> def _get_plant_facts(self):
facts = []
for slot, plant in self._by_slot.items():
tuple_value = ",".join([str(self._arduino_slots.arduino_id),
str(slot),
str(plant["botanical_name"]
... | code_fim | hard | {
"lang": "python",
"repo": "RACPlant/RACAP",
"path": "/controller/plants.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RACPlant/RACAP path: /controller/plants.py
from controller.config import PLANTS_ENDPOINT
from social.arduino import Slots
import pandas as pd
MAX_SLOTS = 10
class Plants:
def __init__(self, arduino_slots: Slots):
self._df = None
self._arduino_slots = arduino_slots
... | code_fim | hard | {
"lang": "python",
"repo": "RACPlant/RACAP",
"path": "/controller/plants.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> training_data = training_data_list[head]
# Define the VI inference technique, ie. minimise the KL divergence between q and p.
inference = ed.KLqp({W_0: qW_0, W_1[head]: qW_1[head],
b_0: qb_0, b_1[head]: qb_1[head]}, data={y[head]:y_ph})
# Initialise the inference... | code_fim | hard | {
"lang": "python",
"repo": "jessegeerts/neural-nets",
"path": "/bayesnets/split_bayes_proper.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> for _ in range(inference.n_iter):
X_batch, Y_batch = training_data.next_batch(batch_size,shuffle=False)
info_dict = inference.update(feed_dict={x: X_batch, y_ph: Y_batch})
inference.print_progress(info_dict)
def take_posterior_samples(n_samples,X_test,testhead,qW_0,qb_0,qW_1,q... | code_fim | hard | {
"lang": "python",
"repo": "jessegeerts/neural-nets",
"path": "/bayesnets/split_bayes_proper.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jessegeerts/neural-nets path: /bayesnets/split_bayes_proper.py
import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns
import tensorflow as tf
from edward.models import Categorical, Normal
import edward as ed
import pandas as pd
from tqdm import tqdm
from mnist_loader import Trai... | code_fim | hard | {
"lang": "python",
"repo": "jessegeerts/neural-nets",
"path": "/bayesnets/split_bayes_proper.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: PyLabCo/pylab-crawler-sdk path: /pylab_crawler_sdk/__init__.py
import requests
BASE_URL = 'https://crawler.pylab.co'
LOG_CRITICAL = 50
LOG_ERROR = 40
LOG_WARNING = 30
LOG_INFO = 20
LOG_DEBUG = 10
LOG_NOTSET = 0
class Session(object):
def __init__(self, key, api_server=BASE_URL):
t... | code_fim | hard | {
"lang": "python",
"repo": "PyLabCo/pylab-crawler-sdk",
"path": "/pylab_crawler_sdk/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def terminate_task(self, task_id) -> None:
"""태스크를 종료"""
res = requests.post(
f'{self.api_server}/api/sdk/task-terminations/?key={self.key}',
data={
'task': task_id
}
)
res.raise_for_status()
def write_file(self, ... | code_fim | hard | {
"lang": "python",
"repo": "PyLabCo/pylab-crawler-sdk",
"path": "/pylab_crawler_sdk/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """태스크를 생성"""
res = requests.post(
f'{self.api_server}/api/sdk/tasks/?key={self.key}',
data={
'taskType': task_type_id
}
)
res.raise_for_status()
task = res.json()
return task['id']
def terminate_task(... | code_fim | hard | {
"lang": "python",
"repo": "PyLabCo/pylab-crawler-sdk",
"path": "/pylab_crawler_sdk/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: matthewhanson/modis-ingestor path: /test/test_main.py
import os
import unittest
import datetime
from dateutil.parser import parse
from modispds.earthdata import query, download_granule
import modispds.main as modis
from modispds.pds import s3_list, del_from_s3
from modispds.products import produc... | code_fim | hard | {
"lang": "python",
"repo": "matthewhanson/modis-ingestor",
"path": "/test/test_main.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_convert_to_geotiff(self):
""" Convert hdf to individual GeoTIFF files """
fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))
for f in fnames:
ext = os.path.splitext(f)[1]
suffix = os.path.splitext(f)[0].split('_'... | code_fim | hard | {
"lang": "python",
"repo": "matthewhanson/modis-ingestor",
"path": "/test/test_main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Convert hdf to individual GeoTIFF files """
fnames = modis.convert_to_geotiff(self.fnames[0], outdir=os.path.dirname(__file__))
for f in fnames:
ext = os.path.splitext(f)[1]
suffix = os.path.splitext(f)[0].split('_')[1]
self.assertTrue(os.pat... | code_fim | hard | {
"lang": "python",
"repo": "matthewhanson/modis-ingestor",
"path": "/test/test_main.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def import_object(self):
ret = MatModuleLevelDocumenter.import_object(self)
# if the class is documented under another name, document it
# as data/attribute
if ret:
if hasattr(self.object, "__name__"):
self.doc_as_attr = self.objpath[-1... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_documenters.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def class_object(self):
# the associated MatClass object
return self.object.cls
def auto_link_self(self, docstrings):
name = self.object.name
# negative look-behind for ` or . or < or * or <non-breaking space>
# and negative look-ahead for <non-break... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_documenters.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sphinx-contrib/matlabdomain path: /sphinxcontrib/mat_documenters.py
docstrings[i][j],
)
return docstrings
def get_object_members(self, want_all):
"""Return `(members_check_module, members)` where `members` is a
list of `(... | code_fim | hard | {
"lang": "python",
"repo": "sphinx-contrib/matlabdomain",
"path": "/sphinxcontrib/mat_documenters.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: GarrettSocling/Pushjet-Server-Api path: /controllers/subscription.py
from flask import Blueprint, jsonify
from utils import Error, has_service, has_uuid, queue_zmq_message
from shared import db
from models import Subscription
from json import dumps as json_encode
from config import zeromq_relay_u... | code_fim | medium | {
"lang": "python",
"repo": "GarrettSocling/Pushjet-Server-Api",
"path": "/controllers/subscription.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> return jsonify({'service': service.as_dict()})
@subscription.route('/subscription', methods=['GET'])
@has_uuid
def subscription_get(client):
subscriptions = Subscription.query.filter_by(device=client).all()
return jsonify({'subscriptions': [_.as_dict() for _ in subscriptions]})
@subscripti... | code_fim | hard | {
"lang": "python",
"repo": "GarrettSocling/Pushjet-Server-Api",
"path": "/controllers/subscription.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> subscriptions = Subscription.query.filter_by(device=client).all()
return jsonify({'subscriptions': [_.as_dict() for _ in subscriptions]})
@subscription.route('/subscription', methods=['DELETE'])
@has_uuid
@has_service
def subscription_delete(client, service):
l = Subscription.query.filter_by... | code_fim | medium | {
"lang": "python",
"repo": "GarrettSocling/Pushjet-Server-Api",
"path": "/controllers/subscription.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>rogramming Language :: Python :: 3.8",
],
entry_points={
'console_scripts': [
'create-python-app=create_python_app.core:main',
]
},
)<|fim_prefix|># repo: averak/create-python-app path: /setup.py
from setuptools import setup, find_packages
try:
long_descriptio... | code_fim | hard | {
"lang": "python",
"repo": "averak/create-python-app",
"path": "/setup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: averak/create-python-app path: /setup.py
from setuptools import setup, find_packages
try:
long_description = open("README.md").read()
except IOError:
long_description = ""
setup(
name="create_python_app",
version="0.1.0",
description="CLI tool to quickstart Python app.",
... | code_fim | medium | {
"lang": "python",
"repo": "averak/create-python-app",
"path": "/setup.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>te_python_app': ['template']},
install_requires=["jinja2"],
long_description=long_description,
classifiers=[
"Programming Language :: Python",
"Programming Language :: Python :: 3.8",
],
entry_points={
'console_scripts': [
'create-python-app=create_p... | code_fim | medium | {
"lang": "python",
"repo": "averak/create-python-app",
"path": "/setup.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ImonEmmanuel/Python_Game path: /Text Adventure/treasure hunt.py
"""
Coding Challenge 3, hangman.py
"""
# Coding Challenge 3, hangman.py
# Name: Eseka Precious
# Student No: 2024170
import time
import random
import os
import csv
import sys
os.system("clear")
inventory = []
def intro():
p... | code_fim | hard | {
"lang": "python",
"repo": "ImonEmmanuel/Python_Game",
"path": "/Text Adventure/treasure hunt.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Play function to ask user if there would quit or still play
"""
while True:
user = input("Play Again? (Y/N) ").upper()
if user == 'Y':
main()
else:
print('Hope to see you again')
sys.exit()
def main():
"""
Main Game D... | code_fim | hard | {
"lang": "python",
"repo": "ImonEmmanuel/Python_Game",
"path": "/Text Adventure/treasure hunt.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Main Game Driver FUnction
"""
name = input('Enter your Name: ')
playagain = "yes"
if playagain == "yes":
intro()
intro_end()
choice1_end()
part_1()
choice2 = attack_or_run()
part_1_1(choice2)
scorex = encounter_1(choice2)
... | code_fim | hard | {
"lang": "python",
"repo": "ImonEmmanuel/Python_Game",
"path": "/Text Adventure/treasure hunt.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: RockySJ/ampo path: /static/ant.py
import numpy as np
class StaticFns:
@staticmethod
def termination_fn(obs, act, next_obs):
<|fim_suffix|> done = ~not_done
done = done[:, None]
return done<|fim_middle|> assert len(obs.shape) == len(next_obs.shape) == len(a... | code_fim | hard | {
"lang": "python",
"repo": "RockySJ/ampo",
"path": "/static/ant.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> x = next_obs[:, 0]
not_done = np.isfinite(next_obs).all(axis=-1) \
* (x >= 0.2) \
* (x <= 1.0)
done = ~not_done
done = done[:, None]
return done<|fim_prefix|># repo: RockySJ/ampo path: /static/ant.py
import numpy as np
class... | code_fim | medium | {
"lang": "python",
"repo": "RockySJ/ampo",
"path": "/static/ant.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> swagger_paths = ['/ui/css', '/ui/lib', '/ui/images', '/swagger.json']
ui = '/' + self._env.get('swagger_ui', 'ui')+'/'
swagger_paths.append(ui)
for path in swagger_paths:
uri = self._env.swagger.base
if len(uri):
... | code_fim | hard | {
"lang": "python",
"repo": "ONSdigital/ras-common",
"path": "/ons_ras_common/ons_registration.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ONSdigital/ras-common path: /ons_ras_common/ons_registration.py
"""
Generic Configuration tool for Micro-Service environment discovery
License: MIT
Copyright (c) 2017 Crown Copyright (Office for National Statistics)
ONSRegistration takes care of registering the Micro-service with t... | code_fim | hard | {
"lang": "python",
"repo": "ONSdigital/ras-common",
"path": "/ons_ras_common/ons_registration.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: salceson/android-copernicus path: /device/device/device.py
#!/usr/bin/env python
# coding=utf-8
__author__ = 'Michał Ciołczyk'
VIRTUAL_COPERNICUS = True
MCAST_GRP = '234.6.6.6'
MCAST_PORT = 3666
FLOOR = '1' # 1 at the moment only
ROOM = 'kitchen' # kitchen|corridor
DELAY = 5 # in seconds
DEB... | code_fim | hard | {
"lang": "python",
"repo": "salceson/android-copernicus",
"path": "/device/device/device.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def thread_func():
global sock, light_on
while True:
command = sock.recv(10240)
if DEBUG:
print command.split(';')
tab = command.split(';')
if len(tab) < 4:
continue
floor = tab[0]
room = tab[1]
device = tab[2]
... | code_fim | hard | {
"lang": "python",
"repo": "salceson/android-copernicus",
"path": "/device/device/device.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: juanshishido/codewars path: /kyu7/tests/test_accum.py
import unittest
from kyu7.accum import accum
class TestAccum(unittest.TestCase):
<|fim_suffix|> self.assertEqual('C-Ww-Aaa-Tttt', accum('cwAt'))<|fim_middle|> def test_abcd(self):
self.assertEqual('A-Bb-Ccc-Dddd', accum('... | code_fim | hard | {
"lang": "python",
"repo": "juanshishido/codewars",
"path": "/kyu7/tests/test_accum.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.assertEqual('R-Qq-Aaa-Eeee-Zzzzz-Tttttt-Yyyyyyy', accum('RqaEzty'))
def test_cwAt(self):
self.assertEqual('C-Ww-Aaa-Tttt', accum('cwAt'))<|fim_prefix|># repo: juanshishido/codewars path: /kyu7/tests/test_accum.py
import unittest
from kyu7.accum import accum
class TestAccum(un... | code_fim | easy | {
"lang": "python",
"repo": "juanshishido/codewars",
"path": "/kyu7/tests/test_accum.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> server_url: str,
cid: int,
talk: Talk,
timeout: Union[Optional[float], Tuple[Optional[float], Optional[float]]] = (3, None),
) -> TalkOnServerResponse:
api_url = urljoin(server_url, f'PLAY2/{int(cid)}')
headers = {
'Content-Type': 'application/json',
}
data = talk... | code_fim | medium | {
"lang": "python",
"repo": "aoirint/jasmine_zinc",
"path": "/jasmine_zinc/talk_on_server.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aoirint/jasmine_zinc path: /jasmine_zinc/talk_on_server.py
from urllib.parse import urljoin
import json
import requests
from dataclasses import dataclass
from typing import Union, Tuple, Optional
from .Talk import (
Talk,
talk2dict,
)
@dataclass
class TalkOnServerResponse:
message: ... | code_fim | medium | {
"lang": "python",
"repo": "aoirint/jasmine_zinc",
"path": "/jasmine_zinc/talk_on_server.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> ret = TalkOnServerResponse(
message=response['message'],
)
return ret<|fim_prefix|># repo: aoirint/jasmine_zinc path: /jasmine_zinc/talk_on_server.py
from urllib.parse import urljoin
import json
import requests
from dataclasses import dataclass
from typing import Union, Tuple, Option... | code_fim | hard | {
"lang": "python",
"repo": "aoirint/jasmine_zinc",
"path": "/jasmine_zinc/talk_on_server.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def books_pipeline():
"""Read Books Corpus filenames and create Beam pipeline."""
# set a random seed for reproducability
rng = random.Random(FLAGS.random_seed)
# BooksCorpus is organized into directories of genre and files of books
# adventure-all.txt seems to contain all the adventure books... | code_fim | hard | {
"lang": "python",
"repo": "google-research/language",
"path": "/language/conpono/create_pretrain_data/books_preproc_pipeline.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: google-research/language path: /language/conpono/create_pretrain_data/books_preproc_pipeline.py
# coding=utf-8
# Copyright 2018 The Google AI Language Team Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the Licens... | code_fim | hard | {
"lang": "python",
"repo": "google-research/language",
"path": "/language/conpono/create_pretrain_data/books_preproc_pipeline.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __str__(self):
return self.user.username<|fim_prefix|># repo: zxzl/dike path: /dike/webdike/models/user_profile.py
from django.db import models
from django.contrib.auth.models import User
from .sentence import Sentence
<|fim_middle|>class UserProfile(models.Model):
user = models.Fo... | code_fim | hard | {
"lang": "python",
"repo": "zxzl/dike",
"path": "/dike/webdike/models/user_profile.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zxzl/dike path: /dike/webdike/models/user_profile.py
from django.db import models
from django.contrib.auth.models import User
from .sentence import Sentence
<|fim_suffix|> user = models.ForeignKey(User)
work = models.ManyToManyField(
Sentence,
through='UserHistory',
... | code_fim | easy | {
"lang": "python",
"repo": "zxzl/dike",
"path": "/dike/webdike/models/user_profile.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return y
# Some useful presets
spikegadgets_lfp_filter_params = {
'dtype': np.int16,
# 'ts_dtype': 'np.uint32',
'fs' : 30000, # sampling rate [Hz]
'fl' : None, # low cut for spike filtering
'fh' : None, # high cut for spike filtering
'gpass' : 0.1, # maximum ... | code_fim | hard | {
"lang": "python",
"repo": "kemerelab/jagular",
"path": "/jagular/filtering.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kemerelab/jagular path: /jagular/filtering.py
"""filtering.py
Temporal filtering for Jagular. We assume that the original data is in (multiple) files
and that they are annoyingly large. So all the methods here work on buffered input,
using memory maps.
This work is based loosely on simila... | code_fim | hard | {
"lang": "python",
"repo": "kemerelab/jagular",
"path": "/jagular/filtering.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> if filter_epochs is None:
filter_epochs = get_contiguous_segments(data=timestamps,
assume_sorted=assume_sorted,
step=step,
index=True)
for ... | code_fim | hard | {
"lang": "python",
"repo": "kemerelab/jagular",
"path": "/jagular/filtering.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zheng568/DECAPS_for_COVID19 path: /models/custom.py
import torch.nn as nn
import torch.nn.functional as F
class Conv2dSame(nn.Module):
def __init__(self, in_channels, out_channels, kernel_size, bias=True, padding_layer=nn.ReflectionPad2d):
super().__init__()
ka = kernel_size... | code_fim | hard | {
"lang": "python",
"repo": "zheng568/DECAPS_for_COVID19",
"path": "/models/custom.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def forward(self, x):
x = F.relu(self.bn1(self.conv1(x)))
x = F.relu(self.bn2(self.conv2(x)))
x = F.relu(self.bn3(self.conv3(x)))
x = F.relu(self.bn4(self.conv4(x)))
x = F.relu(self.bn5(self.conv5(x)))
x = F.relu(self.bn6(self.conv6(x)))
x = F.re... | code_fim | hard | {
"lang": "python",
"repo": "zheng568/DECAPS_for_COVID19",
"path": "/models/custom.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class CustomNet(nn.Module):
def __init__(self):
super(CustomNet, self).__init__()
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=3, padding=1)
self.bn1 = nn.BatchNorm2d(64)
self.conv2 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, strid... | code_fim | medium | {
"lang": "python",
"repo": "zheng568/DECAPS_for_COVID19",
"path": "/models/custom.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: CDH-SC/paragon path: /preproc/build/lib/ocrolib/plotutils.py
################################################################
### Miscellaneous plotting functions based on matplotlib.
################################################################
import matplotlib
import __init__ as ocropy # ... | code_fim | medium | {
"lang": "python",
"repo": "CDH-SC/paragon",
"path": "/preproc/build/lib/ocrolib/plotutils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def plotgrid(data,d=10,shape=(30,30)):
"""Plot a list of images on a grid."""
ion()
gray()
clf()
for i in range(min(d*d,len(data))):
subplot(d,d,i+1)
row = data[i]
if shape is not None: row = row.reshape(shape)
imshow(row)
ginput(1,timeout=0.1)<|fim_... | code_fim | hard | {
"lang": "python",
"repo": "CDH-SC/paragon",
"path": "/preproc/build/lib/ocrolib/plotutils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def unstashCollisions(self):
self.collisionNodePath.unstash()
def __hitFloor(self, entry):
if self.state == 'Dropped' or self.state == 'LocalDropped':
self.d_hitFloor()
self.demand('SlidingFloor', localAvatar.doId)
def __hitGoon(self, entry):
i... | code_fim | hard | {
"lang": "python",
"repo": "open-toontown/open-toontown",
"path": "/toontown/cogdominium/DistCogdoCraneObject.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: open-toontown/open-toontown path: /toontown/cogdominium/DistCogdoCraneObject.py
from panda3d.core import *
from direct.interval.IntervalGlobal import *
from direct.directnotify import DirectNotifyGlobal
from direct.distributed import DistributedSmoothNode
from toontown.toonbase import ToontownGlo... | code_fim | hard | {
"lang": "python",
"repo": "open-toontown/open-toontown",
"path": "/toontown/cogdominium/DistCogdoCraneObject.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|>B = np.dot((u1-u).T, u1-u) * b1
B += np.dot((u2-u).T, u2-u) * b2
B += np.dot((u3-u).T, u3-u) * b3
eig_vals, eig_vecs = np.linalg.eigh(B)
print("evals=", eig_vals, " evecs=", eig_vecs)
id = np.argsort(eig_vals)[:: -1] # sort in reverse order
eig_vals = eig_vals[id]
eig_vecs = eig_vecs[:, id]
... | code_fim | hard | {
"lang": "python",
"repo": "shivachawala/Feature-Extraction",
"path": "/scatter2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: shivachawala/Feature-Extraction path: /scatter2.py
import numpy as np
import sys
if len(sys.argv) != 5:
print("usage:", sys.argv[0], "data_file labels_file")
sys.exit()
# get matrix from file
X = np.genfromtxt(sys.argv[1], delimiter=',', autostrip=True)
y = np.genfromtxt(sys.ar... | code_fim | hard | {
"lang": "python",
"repo": "shivachawala/Feature-Extraction",
"path": "/scatter2.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>eig_vals, eig_vecs = np.linalg.eigh(B)
print("evals=", eig_vals, " evecs=", eig_vecs)
id = np.argsort(eig_vals)[:: -1] # sort in reverse order
eig_vals = eig_vals[id]
eig_vecs = eig_vecs[:, id]
r = 2
eig_vecs_2 = eig_vecs[:, :r]
eig_vec_T = eig_vecs_2.T
np.savetxt(sys.argv[3], eig_vec_T, deli... | code_fim | medium | {
"lang": "python",
"repo": "shivachawala/Feature-Extraction",
"path": "/scatter2.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> '''
825~835MHz,870~880,890~915,935~960,1570.42~1585,
1710~1785,1805~1880,1920~1980,2110~2170,
2570~2620,1880~1915,2300~2400,2400~2483.5
截图三张,一张图最多截10个marker
markerlist:[]
:return:
'''
logger.debug('zvl get_gain_vs_freq')
self... | code_fim | hard | {
"lang": "python",
"repo": "coderdq/vuetest",
"path": "/WEB21-1-12/WEB2/power/zvl_test.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: coderdq/vuetest path: /WEB21-1-12/WEB2/power/zvl_test.py
# coding:utf-8
'''
矢网的测试项，包括增益，带内波动，VSWR
一个曲线最多建10个marker
'''
import os
import logging
from commoninterface.zvlbase import ZVLBase
logger = logging.getLogger('ghost')
class HandleZVL(object):
def __init__(self, ip, offset):
s... | code_fim | hard | {
"lang": "python",
"repo": "coderdq/vuetest",
"path": "/WEB21-1-12/WEB2/power/zvl_test.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """ Make sure the python is current for types.yaml """
self.single_file_generator('py', PythonGenerator, filtr=metadata_filter)
# Make sure the python is valid
with open(os.path.join(self.source_path, 'types.py')) as f:
pydata = f.read()
spec = compile(... | code_fim | hard | {
"lang": "python",
"repo": "lushacao/biolinkml",
"path": "/tests/test_types.py",
"mode": "spm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lushacao/biolinkml path: /tests/test_types.py
import os
import unittest
from types import ModuleType
from biolinkml.generators.pythongen import PythonGenerator
from tests import targetdir
from tests.test_scripts.clicktestcase import metadata_filter
from tests.utils.generator_utils import Generat... | code_fim | medium | {
"lang": "python",
"repo": "lushacao/biolinkml",
"path": "/tests/test_types.py",
"mode": "psm",
"license": "CC0-1.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>OUT_DIR = join(PROJ_DIR, 'out')
os.makedirs(OUT_DIR, exist_ok=True)
OUT_PAPER_DIR = join(OUT_DIR, 'papers')
os.makedirs(OUT_PAPER_DIR, exist_ok=True)
AUTHOR_TYPE = 0
PAPER_TYPE = 1
VENUE_TYPE = 2<|fim_prefix|># repo: sl1296/ai-test path: /xq-oag/core/utils/settings.py
import os
from os.path import join,... | code_fim | hard | {
"lang": "python",
"repo": "sl1296/ai-test",
"path": "/xq-oag/core/utils/settings.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: sl1296/ai-test path: /xq-oag/core/utils/settings.py
import os
from os.path import join, abspath, dirname
<|fim_suffix|>OUT_DIR = join(PROJ_DIR, 'out')
os.makedirs(OUT_DIR, exist_ok=True)
OUT_PAPER_DIR = join(OUT_DIR, 'papers')
os.makedirs(OUT_PAPER_DIR, exist_ok=True)
AUTHOR_TYPE = 0
PAPER_TYP... | code_fim | hard | {
"lang": "python",
"repo": "sl1296/ai-test",
"path": "/xq-oag/core/utils/settings.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: centre-for-humanities-computing/hope_dataprep path: /Preprocessing/extract_nordic_tweets.py
import os
import ndjson
import pandas as pd
"""
Makes daily language specific files in correct format
"""
# define languages to extract
langs = ["da", "no", "sv"]
# make a function that transforms a pa... | code_fim | hard | {
"lang": "python",
"repo": "centre-for-humanities-computing/hope_dataprep",
"path": "/Preprocessing/extract_nordic_tweets.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # loop through the desired language list
for language in langs:
print(f"extract {language}")
# filter data for the desired language using twitter lang tag
df_lang = df[df.lang.eq(language)]
# convert data to ndjson and write it down
print("Writing down..."... | code_fim | hard | {
"lang": "python",
"repo": "centre-for-humanities-computing/hope_dataprep",
"path": "/Preprocessing/extract_nordic_tweets.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: simongarisch/qstrader path: /tests/unit/system/rebalance/test_end_of_month_rebalance.py
import pandas as pd
import pytest
import pytz
from qstrader.system.rebalance.end_of_month import EndOfMonthRebalance
@pytest.mark.parametrize(
"start_date,end_date,pre_market,expected_dates,expected_tim... | code_fim | hard | {
"lang": "python",
"repo": "simongarisch/qstrader",
"path": "/tests/unit/system/rebalance/test_end_of_month_rebalance.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> expected_datetimes = [
pd.Timestamp('%s %s' % (expected_date, expected_time), tz=pytz.UTC)
for expected_date in expected_dates
]
assert actual_datetimes == expected_datetimes<|fim_prefix|># repo: simongarisch/qstrader path: /tests/unit/system/rebalance/test_end_of_month_rebal... | code_fim | medium | {
"lang": "python",
"repo": "simongarisch/qstrader",
"path": "/tests/unit/system/rebalance/test_end_of_month_rebalance.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> reb = EndOfMonthRebalance(
start_dt=sd, end_dt=ed, pre_market=pre_market
)
actual_datetimes = reb._generate_rebalances()
expected_datetimes = [
pd.Timestamp('%s %s' % (expected_date, expected_time), tz=pytz.UTC)
for expected_date in expected_dates
]
asser... | code_fim | hard | {
"lang": "python",
"repo": "simongarisch/qstrader",
"path": "/tests/unit/system/rebalance/test_end_of_month_rebalance.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def copy_dir(src, dest):
try:
shutil.copytree(src, dest)
except OSError as e:
if e.errno == errno.ENOTDIR:
shutil.copy(src, dest)
else:
print('Directory not copied. Error: %s' % e)
def resize_imgs(root):
image_path = os.path.join(root, '*/*/')... | code_fim | hard | {
"lang": "python",
"repo": "amzn/xfer",
"path": "/leap/leap/utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>def random_affine_matrix(img, scale=(0.8, 1.2), rotation=(0, 2*np.pi), translation=(0.2, 0.2)):
angle = np.random.uniform(low=rotation[0], high=rotation[1])
tx = translation[0] * img.size[0]
ty = translation[1] * img.size[1]
translation = (np.round(random.uniform(-tx, tx)), np.round(random... | code_fim | hard | {
"lang": "python",
"repo": "amzn/xfer",
"path": "/leap/leap/utils.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: amzn/xfer path: /leap/leap/utils.py
# Copyright 2020 Amazon.com, Inc. or its affiliates. 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.
# A copy of the License is located at
#
# ... | code_fim | hard | {
"lang": "python",
"repo": "amzn/xfer",
"path": "/leap/leap/utils.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: bhuman/DeepFieldBoundary path: /Training-Pipeline/evaluate.py
import os
os.environ['CUDA_VISIBLE_DEVICES'] = '0'
os.environ['TF_FORCE_GPU_ALLOW_GROWTH'] = 'true'
import sys
sys.path.append(os.path.join(os.path.dirname(__file__), "..", "Utils"))
sys.path.insert(0, os.path.join(os.path.dirname(__fi... | code_fim | hard | {
"lang": "python",
"repo": "bhuman/DeepFieldBoundary",
"path": "/Training-Pipeline/evaluate.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> fitted_predictions = []
fitted_predictions_unit_weight = []
for pred in predictions:
spots = np.concatenate([np.linspace(0, 1, num=len(pred))[:, np.newaxis], pred], axis=1)
fitted_model = fitting.fit_model(spots, step=1)
fitted_predictions.append([label_utils.get_field_... | code_fim | hard | {
"lang": "python",
"repo": "bhuman/DeepFieldBoundary",
"path": "/Training-Pipeline/evaluate.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_ns_pkg_get_one_not_found(self):
resp = self.client.get("/api/catalog/v1/nspackages/22")
self.assertEqual(resp.status_code, status.HTTP_500_INTERNAL_SERVER_ERROR)
self.assertEqual(
{"error": "Ns package[22] not Found."},
resp.data)
#########... | code_fim | hard | {
"lang": "python",
"repo": "onap/modeling-etsicatalog",
"path": "/catalog/packages/tests/test_nspackage.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: onap/modeling-etsicatalog path: /catalog/packages/tests/test_nspackage.py
# Copyright 2017 ZTE Corporation.
#
# 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
#
# ... | code_fim | hard | {
"lang": "python",
"repo": "onap/modeling-etsicatalog",
"path": "/catalog/packages/tests/test_nspackage.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def tearDown(self):
Image.objects.all().delete()
Location.objects.all().delete()
Category.objects.all().delete()
def test_save_image(self):
self.monalisa.saveImage()
images = Image.objects.all()
self.assertTrue(len(images) > 1)<|fim_prefix|># repo: ... | code_fim | hard | {
"lang": "python",
"repo": "KellenNjoroge/keller-gallery",
"path": "/personal/tests.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KellenNjoroge/keller-gallery path: /personal/tests.py
from django.test import TestCase
from .models import Category, Location, Image
class ImageTestClass(TestCase):
def setUp(self):
<|fim_suffix|> def tearDown(self):
Image.objects.all().delete()
Location.objects.all().del... | code_fim | hard | {
"lang": "python",
"repo": "KellenNjoroge/keller-gallery",
"path": "/personal/tests.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.conf = SparkConf()
self.conf.setAppName(appName).setMaster(masterName)
def getSparkConf(self):
return self.conf<|fim_prefix|># repo: rockyCheung/Lambda path: /cobra/spark/SparkConfigSingleton.py
# -*- coding:utf-8 -*-
from pyspark import SparkConf
"""
# 创建单例，初始化SparkConf
... | code_fim | hard | {
"lang": "python",
"repo": "rockyCheung/Lambda",
"path": "/cobra/spark/SparkConfigSingleton.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rockyCheung/Lambda path: /cobra/spark/SparkConfigSingleton.py
# -*- coding:utf-8 -*-
from pyspark import SparkConf
"""
# 创建单例，初始化SparkConf
"""
class Singleton(object):
_instance = None
def __new__(cls, *args, **kw):
if not cls._instance:
cls._instance = super(Singleto... | code_fim | medium | {
"lang": "python",
"repo": "rockyCheung/Lambda",
"path": "/cobra/spark/SparkConfigSingleton.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rsnodgrass/python-anthemav-serial path: /anthemav_serial/protocol_sync.py
"""Simple RS232 communication mechanism for request/reply style commands"""
import logging
import serial
import time
from .const import ASCII, CONF_EOL, CONF_THROTTLE_RATE, CONF_TIMEOUT, DEFAULT_TIMEOUT, FIVE_MINUTES
LO... | code_fim | hard | {
"lang": "python",
"repo": "rsnodgrass/python-anthemav-serial",
"path": "/anthemav_serial/protocol_sync.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> result = bytearray()
while True:
c = self._port.read(1)
if not c:
ret = bytes(result)
LOG.info("Received partial: %s", result)
raise serial.SerialTimeoutException(
'C... | code_fim | hard | {
"lang": "python",
"repo": "rsnodgrass/python-anthemav-serial",
"path": "/anthemav_serial/protocol_sync.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> write = csv.writer(f)
header = []
for th in table_header:
header.append(th.text)
write.writerow(header)
for row in table.find_all('tr'):
body = []
for data in row.find_all('td'):
body.append(data.text)
print(body)
write.writerow(bod... | code_fim | medium | {
"lang": "python",
"repo": "javier89/web_scraping",
"path": "/table.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>l('tr'):
body = []
for data in row.find_all('td'):
body.append(data.text)
print(body)
write.writerow(body)<|fim_prefix|># repo: javier89/web_scraping path: /table.py
import requests
from bs4 import BeautifulSoup
import csv
url = 'https://webscraper.io/test... | code_fim | medium | {
"lang": "python",
"repo": "javier89/web_scraping",
"path": "/table.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: javier89/web_scraping path: /table.py
import requests
from bs4 import BeautifulSoup
import csv
url = 'https://webscraper.io/test-sites/tables'
res = requests.get(url)
soup = BeautifulSoup(res<|fim_suffix|>l('tr'):
body = []
for data in row.find_all('td'):
body.append(... | code_fim | hard | {
"lang": "python",
"repo": "javier89/web_scraping",
"path": "/table.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: therealAYB/peoplelytics path: /preprocessing/incidents.py
import numpy as np
import settings
def mock_incidents(df):
overall_rates = settings.OVERALL_RATES
env_rates = settings.ENVIRONMENT_IMPACT
death_risk = (1 + df['driver_death_add_risk'])*(1 + df['truck_death_add_risk'])*(1 + df... | code_fim | hard | {
"lang": "python",
"repo": "therealAYB/peoplelytics",
"path": "/preprocessing/incidents.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> df['Death'] = np.random.rand(len(df)) < death_risk_adj
df['Major Incident'] = np.random.rand(len(df)) < major_risk_adj
df['Minor Incident'] = np.random.rand(len(df)) < minor_risk_adj
return df<|fim_prefix|># repo: therealAYB/peoplelytics path: /preprocessing/incidents.py
import numpy as n... | code_fim | hard | {
"lang": "python",
"repo": "therealAYB/peoplelytics",
"path": "/preprocessing/incidents.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
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