text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_suffix|># Area to define Main
def main():
print("Hello fellow user! Please provide your input when prompted.\n\n\tSPECIAL NOTE!\n\nWhen done providing input, please type, \"QUIT\" to end capture of input.")
# Special thanks to Thomas Streets Module 4 in developing my thoughts and decreasing my written code ... | code_fim | hard | {
"lang": "python",
"repo": "rfiorenzano/ITS320_CSU_FiorenzanoRogelio",
"path": "/ITS320_CTA5.Option1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|># Area to define String Reversal
def str_reversal():
# Special thanks to our LinkedIn video with kittens and reminding us to "return variable" for later use
usr_strings.reverse()
return usr_strings
str_reversal()
# Area to define Main
def main():
print("Hello fellow user! Please provide your inp... | code_fim | hard | {
"lang": "python",
"repo": "rfiorenzano/ITS320_CSU_FiorenzanoRogelio",
"path": "/ITS320_CTA5.Option1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rfiorenzano/ITS320_CSU_FiorenzanoRogelio path: /ITS320_CTA5.Option1.py
# python3 (3.6)
# Coded on iPad Pro 2020 4th Generation
# Pythonista an Apple iPad App
# MIT License Copyright (c) 2020 Rogelio Fiorenzano
#
# ITS320: Basic Programming
# Colorado State University Global
# Dr. Joseph Turano
<... | code_fim | medium | {
"lang": "python",
"repo": "rfiorenzano/ITS320_CSU_FiorenzanoRogelio",
"path": "/ITS320_CTA5.Option1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MarcusMendes81/Python path: /Ex067 - Tabuada 3.0.py
print('-'*10, 'Tabuada 3.0', '-'*10)
while True:
num = int(input('Digite qua<|fim_suffix|> break
for cont in range(1, 11):
mult = num * cont
print(f'{cont} x {num} = {mult}')
print('Programa de Tabuada encerrad... | code_fim | medium | {
"lang": "python",
"repo": "MarcusMendes81/Python",
"path": "/Ex067 - Tabuada 3.0.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|>nt(f'{cont} x {num} = {mult}')
print('Programa de Tabuada encerrada com sucesso!')<|fim_prefix|># repo: MarcusMendes81/Python path: /Ex067 - Tabuada 3.0.py
print('-'*10, 'Tabuada 3.0', '-'*10)
while True:
num = int(input('Digite qua<|fim_middle|>l o valor da tabuada que voce deseja: '))
print... | code_fim | medium | {
"lang": "python",
"repo": "MarcusMendes81/Python",
"path": "/Ex067 - Tabuada 3.0.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: demartinofra/aws-parallelcluster path: /cli/tests/pcluster/createami/test_pcluster_createami.py
"""This module provides unit tests for (portions of) the `pcluster createami` code."""
import os
import pytest
from assertpy import assert_that
from recordclass import recordclass
import pcluster.cr... | code_fim | hard | {
"lang": "python",
"repo": "demartinofra/aws-parallelcluster",
"path": "/cli/tests/pcluster/createami/test_pcluster_createami.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> mocker, base_ami_id, instance_type, base_ami_os, base_ami_architecture, supported_instance_archs, supported_os
):
"""Verify that parameter validation works as expected in the function that implements the createami command."""
base_ami_name = "ami-x"
mocker.patch("pcluster.createami.utils.g... | code_fim | hard | {
"lang": "python",
"repo": "demartinofra/aws-parallelcluster",
"path": "/cli/tests/pcluster/createami/test_pcluster_createami.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> createami.utils.get_info_for_amis.assert_called_with([base_ami_id])
if instance_type is None:
createami._get_default_createami_instance_type.assert_called_with(base_ami_architecture)
else:
createami.utils.get_supported_architectures_for_instance_type.assert_called_with(instanc... | code_fim | hard | {
"lang": "python",
"repo": "demartinofra/aws-parallelcluster",
"path": "/cli/tests/pcluster/createami/test_pcluster_createami.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ismarou/pyBulletIntro path: /turtleKeyboardMove.py
import pybullet as p
import time
# open the GUI
p.connect(p.GUI)
# load files and place them at the offsets
turtle = p.loadURDF("urdf/most_simple_turtle.urdf",[0,0,0])
plane = p.loadURDF("urdf/plane_box.urdf")
box1 = p.loadURDF("urdf/box.urdf"... | code_fim | medium | {
"lang": "python",
"repo": "ismarou/pyBulletIntro",
"path": "/turtleKeyboardMove.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> rightWheelVelocity+= (forward+turn)*speed
leftWheelVelocity += (forward-turn)*speed
p.setJointMotorControl2(turtle,0,p.VELOCITY_CONTROL,targetVelocity=leftWheelVelocity,force=1000)
p.setJointMotorControl2(turtle,1,p.VELOCITY_CONTROL,targetVelocity=rightWheelVelocity,force=1000)<|fim_prefix|># repo: ... | code_fim | hard | {
"lang": "python",
"repo": "ismarou/pyBulletIntro",
"path": "/turtleKeyboardMove.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: takabayashi/lit path: /test/test_basic.py
#!/usr/bin/env python3
# Copyright (c) 2017 The lit developers
# Distributed under the MIT software license, see the accompanying
# file LICENSE or http://www.opensource.org/licenses/mit-license.php.
"""Test basic lit functionality
- start coin node
- st... | code_fim | hard | {
"lang": "python",
"repo": "takabayashi/lit",
"path": "/test/test_basic.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> litnode0_channel = self.litnodes[0].ChannelList()['result']['Channels'][0]
litnode1_channel = self.litnodes[1].ChannelList()['result']['Channels'][0]
assert_equal(litnode0_channel['MyBalance'], 950000000)
assert_equal(litnode1_channel['MyBalance'], 50000000)
self.l... | code_fim | hard | {
"lang": "python",
"repo": "takabayashi/lit",
"path": "/test/test_basic.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: KamalDGRT/ProgrammingPractice path: /LeetCode/Merge_Interval/solution.py
# intervals = [[1,6],[2,3],[8,10],[15,18]]
# start = 1
# end = 3
# next_start = 2
# next_end = 6
# if next_start less than or equal to end,
# then,
# new_end = max(end, next_end)
# merged_list = [start... | code_fim | hard | {
"lang": "python",
"repo": "KamalDGRT/ProgrammingPractice",
"path": "/LeetCode/Merge_Interval/solution.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> current_start = intervals[pair][0]
current_end = intervals[pair][1]
print("index = ", pair)
print("current_start = ", current_start)
print("current_end = ", current_end)
next_pair = intervals[pair + 1]
next_start = next_pair[0]
next_end = next_pair[1]
print(next_pair, ... | code_fim | medium | {
"lang": "python",
"repo": "KamalDGRT/ProgrammingPractice",
"path": "/LeetCode/Merge_Interval/solution.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # The HEATER is the Receiver
HEATER = Heater()
# Create Commands
SLIDER_MAX = SliderMaxCommand(HEATER)
SLIDER_PERCENT = SliderPercentCommand(HEATER)
SLIDER_OFF = SliderOffCommand(HEATER)
# Register the commands with the invoker (Switch)
SLIDER = Slider()
SLIDER.regist... | code_fim | hard | {
"lang": "python",
"repo": "lentiummmx/Design-Patterns-In-Python",
"path": "/command/slider_command.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lentiummmx/Design-Patterns-In-Python path: /command/slider_command.py
"""The Command Design Pattern in Python
The command pattern is a behavioural design pattern, in which an abstraction
exists between an object that invokes a command, and the object that performs it.
This is part 2 of the Comma... | code_fim | hard | {
"lang": "python",
"repo": "lentiummmx/Design-Patterns-In-Python",
"path": "/command/slider_command.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def close(self):
if self.handle:
xrt.xclClose(self.handle)
self.handle = None
def get_memory(self, desc):
if desc["streaming"]:
if desc["idx"] not in self._streams:
self._streams[desc["idx"]] = XrtStream(self, desc)
retur... | code_fim | hard | {
"lang": "python",
"repo": "schelleg/PYNQ",
"path": "/pynq/pl_server/xrt_device.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: schelleg/PYNQ path: /pynq/pl_server/xrt_device.py
f.base_address = desc["base_address"]
self.desc = desc
self.device = device
def allocate(self, shape, dtype, **kwargs):
"""Create a new buffer in the memory bank
Parameters
----------
shape : ... | code_fim | hard | {
"lang": "python",
"repo": "schelleg/PYNQ",
"path": "/pynq/pl_server/xrt_device.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: schelleg/PYNQ path: /pynq/pl_server/xrt_device.py
ar = PynqBuffer(
shape,
dtype,
bo=bo,
device=device,
buffer=buf,
device_address=device_address,
coherent=False,
)
if pointer is not None:
weakref.finalize(buf, _free_bo, ... | code_fim | hard | {
"lang": "python",
"repo": "schelleg/PYNQ",
"path": "/pynq/pl_server/xrt_device.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: BlueJayADAL/SCARP2020-ML path: /old/matt/models/ANN.py
import tensorflow as tf
from sklearn import preprocessing
from sklearn.model_selection import train_test_split
from old.matt.utils.helper import get_training_data
class ANN:
def __init__(self, training_set, training_anno_file, test_se... | code_fim | hard | {
"lang": "python",
"repo": "BlueJayADAL/SCARP2020-ML",
"path": "/old/matt/models/ANN.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> return X_train_scaled, X_test_scaled, y_train, y_test
def train_model(self):
X_train, X_test, y_train, y_test = self.prep_training_data()
# Create ANN classifier
model = tf.keras.models.Sequential()
model.add(tf.keras.layers.Flatten())
model.add(tf.ker... | code_fim | hard | {
"lang": "python",
"repo": "BlueJayADAL/SCARP2020-ML",
"path": "/old/matt/models/ANN.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> if self.parseVitoria(bot, message) :
self.nivelMaxLimit = self.nivelMaxLimit + 0.9
bot.stamina = bot.stamina - 1
#self.limitLvlRnkThreshold = 0.0
self.feedback = True
def act(self, bot):
print("Act Batalhaarena ...")
if ... | code_fim | hard | {
"lang": "python",
"repo": "fefurst/NotSoIdleTown",
"path": "/states/batalhaarena.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fefurst/NotSoIdleTown path: /states/batalhaarena.py
from .state import State
import re
import constantes
class Batalhaarena(State):
"""
Comportamento associado à tela Menu.
"""
__instance = None
def __new__(cls):
if Batalhaarena.__instance is None:
Batal... | code_fim | hard | {
"lang": "python",
"repo": "fefurst/NotSoIdleTown",
"path": "/states/batalhaarena.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: icgood/pymap path: /test/server/test_store.py
from .base import TestBase
from pymap.imap import IMAPServer
class TestStore(TestBase):
async def test_store(self, imap_server: IMAPServer) -> None:
transport = self.new_transport(imap_server)
transport.push_login()
tr... | code_fim | hard | {
"lang": "python",
"repo": "icgood/pymap",
"path": "/test/server/test_store.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> async def test_store_remove_recent(self, imap_server: IMAPServer) -> None:
transport = self.new_transport(imap_server)
transport.push_login()
transport.push_select(b'INBOX')
transport.push_readline(
b'store1 STORE * -FLAGS (\\Recent)\r\n')
transport.... | code_fim | hard | {
"lang": "python",
"repo": "icgood/pymap",
"path": "/test/server/test_store.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> from . import error_handler
# bind error handler with newly created flask application
error_handler.init_app(app)
from . import api
# bind api with newly created flask application
app.register_blueprint(api.bp)
from . import engine
# bind engine with newly created flask a... | code_fim | medium | {
"lang": "python",
"repo": "randilfernando/bot",
"path": "/bot/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> from . import engine
# bind engine with newly created flask application
engine.init_app(app)
# initialize engine
with app.app_context():
engine.init_bot()
return app<|fim_prefix|># repo: randilfernando/bot path: /bot/__init__.py
import os
from builtins import KeyError, O... | code_fim | hard | {
"lang": "python",
"repo": "randilfernando/bot",
"path": "/bot/__init__.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: randilfernando/bot path: /bot/__init__.py
import os
from builtins import KeyError, OSError
from flask import Flask
def create_app(test_config=None):
# create and configure the bot
app = Flask(__name__, instance_relative_config=True)
# ensure the instance folder exists
try:
... | code_fim | hard | {
"lang": "python",
"repo": "randilfernando/bot",
"path": "/bot/__init__.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mlbench/mlbench-benchmarks path: /pytorch/nlp/translation/wmt16-gnmt-all-reduce/main.py
"""Training GNMT for WMT16 Dataset
This implements the Machine Translation task 4b
see https://mlbench.readthedocs.io/en/latest/benchmark-tasks.html#a-lstm-wmt16-en-de
for more details.
Model and training ta... | code_fim | hard | {
"lang": "python",
"repo": "mlbench/mlbench-benchmarks",
"path": "/pytorch/nlp/translation/wmt16-gnmt-all-reduce/main.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> fp_optimizer, optimizer, model = build_optimizer(
model=model,
math=math_mode,
loss_scaling=loss_scaling,
use_cuda=use_cuda,
use_horovod=use_horovod,
**optimizer_args
)
# Create a learning rate scheduler for an optimizer
scheduler = Exponent... | code_fim | hard | {
"lang": "python",
"repo": "mlbench/mlbench-benchmarks",
"path": "/pytorch/nlp/translation/wmt16-gnmt-all-reduce/main.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> metrics_values, loss = validation_round(
val_loader,
metrics,
model,
criterion,
update_freq,
translator,
use_cuda=use_cuda,
)
is_best = record_validation_sta... | code_fim | hard | {
"lang": "python",
"repo": "mlbench/mlbench-benchmarks",
"path": "/pytorch/nlp/translation/wmt16-gnmt-all-reduce/main.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: aroden-crowdstrike/eamcsv2json path: /eamcsv2json/eamcsv2dict.py
"""
Converts EAM CSV to python dictionaries
"""
import collections
import csv
import logging
logger = logging.getLogger(__name__)
class EamCsv2Dict(object):
"""
Handles converting from an input file to generator of dicti... | code_fim | hard | {
"lang": "python",
"repo": "aroden-crowdstrike/eamcsv2json",
"path": "/eamcsv2json/eamcsv2dict.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> def convert(self):
self._running = True
with self._file_reader as r:
csv_reader = (
csv.reader(
r,
delimiter=',',
doublequote=False,
escapechar='\\',
quotecha... | code_fim | hard | {
"lang": "python",
"repo": "aroden-crowdstrike/eamcsv2json",
"path": "/eamcsv2json/eamcsv2dict.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> add_liked_songs(track_object,x)
likedSong.append(track_object)
@staticmethod
def prepare_image(filename, size):
icon = Image.open('images/'+filename)
icon = icon.resize((size, size), Image.ANTIALIAS)
icon = ImageTk.PhotoImage(icon)
return icon<|fim... | code_fim | hard | {
"lang": "python",
"repo": "yportne8/TK-Player",
"path": "/Pages/SearchPage/Components/LikeButton.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yportne8/TK-Player path: /Pages/SearchPage/Components/LikeButton.py
import tkinter as tk
from PIL import Image, ImageTk
class LikeButton(tk.Button):
def __init__(self, master, *args, **kwargs):
self.title = kwargs.pop('title')
self.album = kwargs.pop('album')
self.ur... | code_fim | hard | {
"lang": "python",
"repo": "yportne8/TK-Player",
"path": "/Pages/SearchPage/Components/LikeButton.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return
# if liked
self['image'] = self.filled_heart
self.liked = False
from Database.Database import add_liked_songs
track_object = {
'title': self.title,
'genre': self.album,
'artist': self.artist,
'locati... | code_fim | hard | {
"lang": "python",
"repo": "yportne8/TK-Player",
"path": "/Pages/SearchPage/Components/LikeButton.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Make coordinates along each axis
x = ox + np.cumsum(self.tensor_u)
x = np.insert(x, 0, ox)
y = oy + np.cumsum(self.tensor_v)
y = np.insert(y, 0, oy)
z = oz + np.cumsum(self.tensor_w)
z = np.insert(z, 0, oz)
# If axis orientations are stand... | code_fim | hard | {
"lang": "python",
"repo": "banesullivan/omf",
"path": "/omf/volume.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def toVTK(self):
"""Convert the 3D gridded volume to a ``vtkStructuredGrid``
(or a ``vtkRectilinearGrid`` when apprropriate) object contatining the
2D surface.
"""
import vtk
from vtk.util import numpy_support as nps
self._validate_mesh()
... | code_fim | hard | {
"lang": "python",
"repo": "banesullivan/omf",
"path": "/omf/volume.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: banesullivan/omf path: /omf/volume.py
"""volume.py: Volume element and geometry"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from __future__ import unicode_literals
import numpy as np
import properties
from .base import ProjectE... | code_fim | hard | {
"lang": "python",
"repo": "banesullivan/omf",
"path": "/omf/volume.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: t-zhong/WaPIRL path: /models/head.py
# -*- coding: utf-8 -*-
import collections
import torch
import torch.nn as nn
from models.base import HeadBase
from layers.core import Flatten
from utils.initialization import initialize_weights
class LinearHead(HeadBase):
def __init__(self, in_channe... | code_fim | hard | {
"lang": "python",
"repo": "t-zhong/WaPIRL",
"path": "/models/head.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def __init__(self, in_channels: int, num_features: int):
"""
Arguments:
in_channels: int, number of input feature maps.
num_features: int, number of output units.
"""
super(MLPHead, self).__init__(num_features)
self.in_channels = in_chan... | code_fim | hard | {
"lang": "python",
"repo": "t-zhong/WaPIRL",
"path": "/models/head.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>class LinearClassifier(LinearHead):
def __init__(self, in_channels: int, num_classes: int, dropout: float = 0.):
"""
Arguments:
in_channels: int, number of input feature maps.
num_classes: int, number of classes.
"""
super(LinearClassifier, self)... | code_fim | hard | {
"lang": "python",
"repo": "t-zhong/WaPIRL",
"path": "/models/head.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Pr0Ger/SGSB path: /plugins/Zombie_Driver.py
import os
from lib.base_plugin import BasePlugin
class ZombieDriverPlugin(BasePlugin):
Name = "Zombie Driver"
support_os = ["Windows"]
<|fim_suffix|> if os.path.isdir(os.path.join(os.environ['APPDATA'], 'ZombieDriver')):
re... | code_fim | hard | {
"lang": "python",
"repo": "Pr0Ger/SGSB",
"path": "/plugins/Zombie_Driver.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> _.restore_files('Saves', os.path.join(os.environ['APPDATA'], 'ZombieDriver'), [
'controller.cfg',
'Ogre17.cfg',
'ZombieDriver.cfg',
])
_.restore_folder('Saves', os.path.join(os.environ['APPDATA'], 'ZombieDriver'), 'Save')
def detect(self):
... | code_fim | hard | {
"lang": "python",
"repo": "Pr0Ger/SGSB",
"path": "/plugins/Zombie_Driver.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
def pycallback():
return True
if __name__ == "__main__":
b = BetterChrome('C:/selenium/chromedriver.exe')
b.get('http://www.google.com')
#b.set_script_timeout(5) # apparently loading jquery requires a page
# load timeout AND async script execution
... | code_fim | hard | {
"lang": "python",
"repo": "lochnessduck/Browser_Automatron",
"path": "/python/BetterChrome.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lochnessduck/Browser_Automatron path: /python/BetterChrome.py
from __future__ import print_function
from selenium.webdriver import Chrome
from js import js # from module js (js.py) import class js.
class BetterChrome(Chrome):
<|fim_suffix|>if __name__ == "__main__":
b = BetterChrome('C:/se... | code_fim | hard | {
"lang": "python",
"repo": "lochnessduck/Browser_Automatron",
"path": "/python/BetterChrome.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>x1 = Variable('x1', lb=0)
x2 = Variable('x2', lb=0)
x3 = Variable('x3', lb=0)
c1 = Constraint(x1 + x2 + x3, ub=100)
c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600)
c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300)
obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max')
model = Model(name='Simple mo... | code_fim | medium | {
"lang": "python",
"repo": "opencobra/optlang",
"path": "/examples/simple.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: opencobra/optlang path: /examples/simple.py
# Copyright 2013 Novo Nordisk Foundation Center for Biosustainability,
# Technical University of Denmark.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may ob... | code_fim | medium | {
"lang": "python",
"repo": "opencobra/optlang",
"path": "/examples/simple.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Ujinjinjin/lars path: /tests/test_lars.py
from pytest import raises
from lars.main import LarsTest
def test_lars():
# test lars without any subcommands or arguments
with LarsTest() as app:
app.run()
assert app.exit_code == 0
<|fim_suffix|> # test apps list with argum... | code_fim | hard | {
"lang": "python",
"repo": "Ujinjinjin/lars",
"path": "/tests/test_lars.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # test apps list with arguments
argv = ['apps', 'list', '-e']
with LarsTest(argv=argv) as cli:
cli.run()
data, output = cli.last_rendered
assert cli.pargs.extended is True
assert data is not None, data
assert len(data['items']) == 1
app = data['i... | code_fim | hard | {
"lang": "python",
"repo": "Ujinjinjin/lars",
"path": "/tests/test_lars.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stscicrawford/test_jwst_rtd path: /jwst/tests_nightly/general/nirspec/test_detector1.py
import pytest
from astropy.io import fits
from jwst.pipeline.calwebb_detector1 import Detector1Pipeline
pytestmark = [
pytest.mark.usefixtures('_jail'),
pytest.mark.skipif(not pytest.config.getoption(... | code_fim | hard | {
"lang": "python",
"repo": "stscicrawford/test_jwst_rtd",
"path": "/jwst/tests_nightly/general/nirspec/test_detector1.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Compare countrate image product
n_cr = 'jw84600007001_02101_00001_nrs1_rate.fits'
h = fits.open( n_cr )
n_ref = _bigdata+'/pipelines/jw84600007001_02101_00001_nrs1_rate_ref.fits'
href = fits.open( n_ref )
newh = fits.HDUList([h['primary'],h['sci'],h['err'],h['dq']])
newhref =... | code_fim | hard | {
"lang": "python",
"repo": "stscicrawford/test_jwst_rtd",
"path": "/jwst/tests_nightly/general/nirspec/test_detector1.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kartikprabhu/hfeed2atom path: /hfeed2atom/templates.py
from string import Template
from . import __about__
GENERATOR = Template("""<generator uri="${uri}" version="${version}">${name}</generator>""").substitute(uri = __about__.URL['self'], version = '.'.join(map(str, __about__.VERSION[0:3])) + '... | code_fim | hard | {
"lang": "python",
"repo": "kartikprabhu/hfeed2atom",
"path": "/hfeed2atom/templates.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ID = Template("""<id>${uid}</id>""")
AUTHOR = Template("""<author><name>${name}</name></author>""")
FEATURED = Template("""<img src="${featured}"/>""")
POST_SUMMARY = Template("""<p>${post_summary}</p>""")
MORELINK = Template("""<span>Full post: <a href="${url}">${name}&l... | code_fim | medium | {
"lang": "python",
"repo": "kartikprabhu/hfeed2atom",
"path": "/hfeed2atom/templates.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>POST_SUMMARY = Template("""<p>${post_summary}</p>""")
MORELINK = Template("""<span>Full post: <a href="${url}">${name}</a></span>""")
SUMMARY = Template("""<summary type="html">${featured}${summary}${morelink}</summary>""")
CONTENT = Template("""<content type="html">... | code_fim | medium | {
"lang": "python",
"repo": "kartikprabhu/hfeed2atom",
"path": "/hfeed2atom/templates.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: AngelBrisco/SubPixelConvolution-in-Keras path: /TF 1 legacy model/Custom upsample layers.py
import numpy as np
from tensorflow.keras.layers import *
from tensorflow.keras import backend as K
import tensorflow as tf
__all__ =["SubpixelLayer2D","conv_up","SubpixelLayer2D_log"]
class SubpixelLayer... | code_fim | hard | {
"lang": "python",
"repo": "AngelBrisco/SubPixelConvolution-in-Keras",
"path": "/TF 1 legacy model/Custom upsample layers.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> self.kernel = self.add_weight(shape=(self.ksz,self.ksz,cin.value,self.out_channels),
initializer=self.kinit,
name='kernel')
super(SubpixelLayer2D, self).build(input_shape)
def call(self,input):
y = K.... | code_fim | hard | {
"lang": "python",
"repo": "AngelBrisco/SubPixelConvolution-in-Keras",
"path": "/TF 1 legacy model/Custom upsample layers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> #Multiplica el kernel para evitar efecto tablero. Aunque no lo creas lo entendiste
y=tf.initializers.variance_scaling()(shape=(h,w,cin,cout))
y=tf.tile(y,[1,1,1,self.prime_scale**2])
sp_weights=tf.Variable(y,
dtype=dtype,
... | code_fim | hard | {
"lang": "python",
"repo": "AngelBrisco/SubPixelConvolution-in-Keras",
"path": "/TF 1 legacy model/Custom upsample layers.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> assert LifReader(resources_dir / filename).get_physical_pixel_size(
scene
) == pytest.approx(expected, rel=0.001)
# Check that there are no open file pointers
assert str(f) not in [f.path for f in proc.open_files()]
@pytest.mark.parametrize(
"filename, s, t, c, z, y, x",
... | code_fim | hard | {
"lang": "python",
"repo": "fabian19941220-gmail-com/aicsimageio",
"path": "/aicsimageio/tests/readers/test_lif_reader.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fabian19941220-gmail-com/aicsimageio path: /aicsimageio/tests/readers/test_lif_reader.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
from io import BytesIO
import numpy as np
import pytest
from psutil import Process
from aicsimageio.readers.lif_reader import LifReader
@pytest.mark.parametr... | code_fim | hard | {
"lang": "python",
"repo": "fabian19941220-gmail-com/aicsimageio",
"path": "/aicsimageio/tests/readers/test_lif_reader.py",
"mode": "psm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Get file
f = resources_dir / filename
# Check that there are no open file pointers
proc = Process()
assert str(f) not in [f.path for f in proc.open_files()]
# Init reader
img = LifReader(f)
# Check that there are no open file pointers after init
proc = Process()
... | code_fim | hard | {
"lang": "python",
"repo": "fabian19941220-gmail-com/aicsimageio",
"path": "/aicsimageio/tests/readers/test_lif_reader.py",
"mode": "spm",
"license": "BSD-3-Clause",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lili2311/sentry path: /tests/sentry/integrations/gitlab/test_webhook.py
from __future__ import absolute_import
from sentry.testutils import APITestCase
import pytest
class WebhookTest(APITestCase):
url = '/extensions/gitlab/webhook'
@pytest.mark.incomplete
def test_get(self):
... | code_fim | hard | {
"lang": "python",
"repo": "lili2311/sentry",
"path": "/tests/sentry/integrations/gitlab/test_webhook.py",
"mode": "psm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> @pytest.mark.incomplete
def test_merge_event_create_commits(self):
pass
@pytest.mark.incomplete
def test_merge_event_create_commits_more_than_20(self):
pass
@pytest.mark.incomplete
def test_merge_event_link_author(self):
pass<|fim_prefix|># repo: lili2311/... | code_fim | medium | {
"lang": "python",
"repo": "lili2311/sentry",
"path": "/tests/sentry/integrations/gitlab/test_webhook.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> pass
@pytest.mark.incomplete
def test_push_event_suspect_commit(self):
pass
@pytest.mark.incomplete
def test_merge_event_create_repo(self):
pass
@pytest.mark.incomplete
def test_merge_event_create_commits(self):
pass
@pytest.mark.incomplete
... | code_fim | medium | {
"lang": "python",
"repo": "lili2311/sentry",
"path": "/tests/sentry/integrations/gitlab/test_webhook.py",
"mode": "spm",
"license": "BSD-2-Clause",
"source": "the-stack-v2"
} |
<|fim_suffix|> pos_outputs: torch.Tensor,
neg_outputs: torch.Tensor,
mask: torch.Tensor = None) -> torch.Tensor:
r"""feed forward of pointwise logistic ranking loss by calculating
:math:`\text{loss} = (1.0 - \sigma (y_{pos})) + \sigma (y_{neg})`
... | code_fim | medium | {
"lang": "python",
"repo": "zwcdp/torecsys",
"path": "/torecsys/losses/ltr/pointwise_ranking_loss.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zwcdp/torecsys path: /torecsys/losses/ltr/pointwise_ranking_loss.py
r"""torecsys.models.ltr.losses.pointwise_ranking_loss is a sub module of algorithms of
pointwise ranking loss
"""
import torch
from . import _RankingLoss
from .functional import apply_mask, pointwise_logistic_ranking_loss
cl... | code_fim | medium | {
"lang": "python",
"repo": "zwcdp/torecsys",
"path": "/torecsys/losses/ltr/pointwise_ranking_loss.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
class PointwiseLogisticLoss(_PointwiseRankingLoss):
r"""pointwise logistic loss
"""
def __init__(self):
super(PointwiseLogisticLoss, self).__init__()
def forward(self,
pos_outputs: torch.Tensor,
neg_outputs: torch.Tensor,
mask: tor... | code_fim | medium | {
"lang": "python",
"repo": "zwcdp/torecsys",
"path": "/torecsys/losses/ltr/pointwise_ranking_loss.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: MLStruckmann/tensorflow-templates path: /preprocessing/preprocessing-nlp.py
import tensorflow as tf
from tensorflow import keras
vocab = ["<1H OCEAN", "INLAND", "NEAR OCEAN", "NEAR BAY", "ISLAND"]
indices = tf.range(len(vocab), dtype=tf.int64)
table_init = tf.lookup.KeyValueTensorInitialize... | code_fim | hard | {
"lang": "python",
"repo": "MLStruckmann/tensorflow-templates",
"path": "/preprocessing/preprocessing-nlp.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>regular_inputs = keras.layers.Input(shape=[8])
categories = keras.layers.Input(shape=[], dtype=tf.string)
cat_indices = keras.layers.Lambda(lambda cats: table.lookup(cats))(categories)
cat_embed = keras.layers.Embedding(input_dim=6, output_dim=2)(cat_indices)
encoded_inputs = keras.layers.concatenate(... | code_fim | medium | {
"lang": "python",
"repo": "MLStruckmann/tensorflow-templates",
"path": "/preprocessing/preprocessing-nlp.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Roberto-Sartore/Python path: /exercicios/PythonExercicios/ex051.py
num = int(input('Primeiro termo:'))
razao = int(input('Razão:'))
decimo = num + (10 - 1) * razao
<|fim_suffix|>print('{} '.format(c), end='-> ')
print('Acabou')<|fim_middle|>for c in range(num, decimo + razao, razao):
| code_fim | easy | {
"lang": "python",
"repo": "Roberto-Sartore/Python",
"path": "/exercicios/PythonExercicios/ex051.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>print('{} '.format(c), end='-> ')
print('Acabou')<|fim_prefix|># repo: Roberto-Sartore/Python path: /exercicios/PythonExercicios/ex051.py
num = int(input('Primeiro termo:'))
razao = int(input('Razão:'))
decimo = num + (10 - 1) * razao
<|fim_middle|>for c in range(num, decimo + razao, razao):
| code_fim | easy | {
"lang": "python",
"repo": "Roberto-Sartore/Python",
"path": "/exercicios/PythonExercicios/ex051.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def send_signal(self, signal: int) -> None: ...
def terminate(self) -> None: ...
def kill(self) -> None: ...
def __enter__(self) -> 'Popen': ...
def __exit__(self, type, value, traceback) -> bool: ...
def getstatusoutput(cmd: str) -> Tuple[int, str]: ...
def getoutput(cmd: str) -> str... | code_fim | hard | {
"lang": "python",
"repo": "Vedenin/intellij-community",
"path": "/python/helpers/typeshed/stdlib/2/subprocess.pyi",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Vedenin/intellij-community path: /python/helpers/typeshed/stdlib/2/subprocess.pyi
# Stubs for subprocess
# Based on http://docs.python.org/2/library/subprocess.html and Python 3 stub
from typing import Sequence, Any, AnyStr, Mapping, Callable, Tuple, IO, Union, Optional
_FILE = Union[int, IO[A... | code_fim | hard | {
"lang": "python",
"repo": "Vedenin/intellij-community",
"path": "/python/helpers/typeshed/stdlib/2/subprocess.pyi",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> def wait(self) -> int: ...
def communicate(self, input: Optional[AnyStr] = ...) -> Tuple[Optional[bytes], Optional[bytes]]: ...
def send_signal(self, signal: int) -> None: ...
def terminate(self) -> None: ...
def kill(self) -> None: ...
def __enter__(self) -> 'Popen': ...
def _... | code_fim | hard | {
"lang": "python",
"repo": "Vedenin/intellij-community",
"path": "/python/helpers/typeshed/stdlib/2/subprocess.pyi",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lanking520/djl-demo path: /aws/inferentia/trace.py
import torch
import os
import torch_neuron
from torchvision import models
import logging
# Enable logging so we can see any important warnings
logger = logging.getLogger('Neuron')
logger.setLevel(logging.INFO)
# An example input you would norma... | code_fim | hard | {
"lang": "python",
"repo": "lanking520/djl-demo",
"path": "/aws/inferentia/trace.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Export to saved model
os.makedirs("models/inferentia/resnet50", exist_ok=True)
model_neuron.save("models/inferentia/resnet50/resnet50.pt")
print("Compile success")<|fim_prefix|># repo: lanking520/djl-demo path: /aws/inferentia/trace.py
import torch
import os
import torch_neuron
from torchvision import ... | code_fim | hard | {
"lang": "python",
"repo": "lanking520/djl-demo",
"path": "/aws/inferentia/trace.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|># Save the Regular TorchScript model for benchmarking
os.makedirs("models/djl/resnet50", exist_ok=True)
djl_traced_model.save("models/djl/resnet50/resnet50.pt")
# Analyze the model - this will show operator support and operator count
torch.neuron.analyze_model(model, example_inputs=[image])
# Now compil... | code_fim | hard | {
"lang": "python",
"repo": "lanking520/djl-demo",
"path": "/aws/inferentia/trace.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: liuzhennn/Ryu_SDN_Controller path: /build/lib.linux-x86_64-2.7/ryu/app/DB2SDN/test.py
import userDao
def test():
# userDao.Dbutil().insert(1,'10.1.0.2','10.3.0.1','g-g-g-g')
<|fim_suffix|>0.3.0.1')
print(result)
test()
print 1*None<|fim_middle|> result=userDao.Dbutil().select('10.1... | code_fim | easy | {
"lang": "python",
"repo": "liuzhennn/Ryu_SDN_Controller",
"path": "/build/lib.linux-x86_64-2.7/ryu/app/DB2SDN/test.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> result=userDao.Dbutil().select('10.1.0.1','10.3.0.1')
print(result)
test()
print 1*None<|fim_prefix|># repo: liuzhennn/Ryu_SDN_Controller path: /build/lib.linux-x86_64-2.7/ryu/app/DB2SDN/test.py
import userDao
def test():
# userDao.Dbu<|fim_middle|>til().insert(1,'10.1.0.2','10.3.0.1','g-... | code_fim | easy | {
"lang": "python",
"repo": "liuzhennn/Ryu_SDN_Controller",
"path": "/build/lib.linux-x86_64-2.7/ryu/app/DB2SDN/test.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jtraver/dev path: /python3/matplotlib/plot1.py
#!/usr/bin/env python3
#!/usr/bin/python
<|fim_suffix|>import numpy
import matplotlib.pyplot as plt
from numpy.random import rand
a = rand(100)
b = rand(100)
plt.scatter(a, b)
plt.show()<|fim_middle|># https://en.wikipedia.org/wiki/Matplotlib
| code_fim | easy | {
"lang": "python",
"repo": "jtraver/dev",
"path": "/python3/matplotlib/plot1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: jtraver/dev path: /python3/matplotlib/plot1.py
#!/usr/bin/env python3
#!/usr/bin/python
# https://en.wikipedia.org/wiki/Matplotlib
import numpy
import matplotlib.pyplot as plt
<|fim_suffix|>a = rand(100)
b = rand(100)
plt.scatter(a, b)
plt.show()<|fim_middle|>from numpy.random import rand
| code_fim | easy | {
"lang": "python",
"repo": "jtraver/dev",
"path": "/python3/matplotlib/plot1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>from numpy.random import rand
a = rand(100)
b = rand(100)
plt.scatter(a, b)
plt.show()<|fim_prefix|># repo: jtraver/dev path: /python3/matplotlib/plot1.py
#!/usr/bin/env python3
#!/usr/bin/python
<|fim_middle|># https://en.wikipedia.org/wiki/Matplotlib
import numpy
import matplotlib.pyplot as plt
| code_fim | medium | {
"lang": "python",
"repo": "jtraver/dev",
"path": "/python3/matplotlib/plot1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Returns
-------
lats : 2d array
latitudes
lons : 2d array
longitudes
Usage
-----
lats,lons = readPiomas(directory,years,threshold)
"""
print '\n>>> Using readGrid25 function!'
### Import modules
import numpy as np
### Read bin... | code_fim | hard | {
"lang": "python",
"repo": "zmlabe/SeaIceVariability",
"path": "/Scripts/calc_grid25.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zmlabe/SeaIceVariability path: /Scripts/calc_grid25.py
"""
Script reads Sea Ice Concentrations from Nimbus-7 SMMR and DMSP SSM/I-SSMIS
Passive Microwave Data, Version 1 binary files for select variables and
regrids according to selected grid style (e.g., NSIDC EASE grid data).
Notes
-----
... | code_fim | hard | {
"lang": "python",
"repo": "zmlabe/SeaIceVariability",
"path": "/Scripts/calc_grid25.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: terpyPy/ButtonBox path: /boardStateDriver/boardFunc/redrawBoard.py
def redrawBoard(theBoard, event):
#
# dict that contains the button numbers and corresponding neighbor cells
neighbors = {
0:[4, 1],
1:[5, 2, 0],
2:[1, 6, 3],
... | code_fim | hard | {
"lang": "python",
"repo": "terpyPy/ButtonBox",
"path": "/boardStateDriver/boardFunc/redrawBoard.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>value of each neighbor cell and turn it on
if theBoard[neighbors[keyPressed][i]] == 0:
newBoard[neighbors[keyPressed][i]] = 1
elif theBoard[neighbors[keyPressed][i]] == 1:
newBoard[neighbors[keyPressed][i]] = 0
print(event.number)
# return the new board
... | code_fim | hard | {
"lang": "python",
"repo": "terpyPy/ButtonBox",
"path": "/boardStateDriver/boardFunc/redrawBoard.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_blacklist_file_only_comments(self):
self.write_blacklist_file(
"# wumbo",
"# mini",
)
results = run_nose(
'--with-blacklist',
'--blacklist-file=%s' % self.blacklist_filepath,
)
expected_test_list = set([
... | code_fim | hard | {
"lang": "python",
"repo": "kse201/nose-blacklist",
"path": "/tests/test_blacklist.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: kse201/nose-blacklist path: /tests/test_blacklist.py
import os
import unittest
import uuid
from utils import run_cmd, Results, TEST_DIR, rm_file
def run_nose(*args):
"""Run nose against our sample dir of tests."""
cmd = ['nosetests'] + list(args) + ['-v', TEST_DIR]
_, err, _ = run_... | code_fim | hard | {
"lang": "python",
"repo": "kse201/nose-blacklist",
"path": "/tests/test_blacklist.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> def write_blacklist_file(self, *lines):
with open(self.blacklist_filepath, 'w') as f:
f.write("\n".join(lines))
def test_empty_blacklist_file(self):
self.write_blacklist_file("")
results = run_nose(
'--with-blacklist',
'--blacklist-file=... | code_fim | hard | {
"lang": "python",
"repo": "kse201/nose-blacklist",
"path": "/tests/test_blacklist.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>with open('捉鬼实习生.txt', 'w') as of:
for i in range(1,8):
url = base_url%i
req = requests.get(url)
content = req.content.decode('gbk')
soup = BeautifulSoup(content)
chapters = soup.find(class_='chapter')
# import pdb;pdb.set_trace()
for chapter in... | code_fim | hard | {
"lang": "python",
"repo": "R11happy/misc",
"path": "/download_book.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> content = req.content.decode('gbk')
soup = BeautifulSoup(content)
chapters = soup.find(class_='chapter')
# import pdb;pdb.set_trace()
for chapter in chapters.find_all('li'):
chaptername = chapter.find('a')
if not chaptername:
... | code_fim | hard | {
"lang": "python",
"repo": "R11happy/misc",
"path": "/download_book.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: R11happy/misc path: /download_book.py
#!usr/bin/env python
#encoding=utf-8
import requests
from bs4 import BeautifulSoup
# 捉鬼实习生
site_url = 'http://m.ybdu.com'
base_url = 'http://m.ybdu.com/xiaoshuo/8/8438_%d/'
<|fim_suffix|>with open('捉鬼实习生.txt', 'w') as of:
for i in range(1,8):
u... | code_fim | hard | {
"lang": "python",
"repo": "R11happy/misc",
"path": "/download_book.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thdb-theo/Board-Games path: /NaC/difs/dif1.py
import random
def completely_random(*args):
common_board = args[0]
available_moves<|fim_suffix|>ot x]
return random.choice(available_moves)<|fim_middle|> = [i for i, x in enumerate(common_board) if n | code_fim | easy | {
"lang": "python",
"repo": "thdb-theo/Board-Games",
"path": "/NaC/difs/dif1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thdb-theo/Board-Games path: /NaC/difs/dif1.py
import random
def completely_random(*args):
<|fim_suffix|>ot x]
return random.choice(available_moves)<|fim_middle|> common_board = args[0]
available_moves = [i for i, x in enumerate(common_board) if n | code_fim | medium | {
"lang": "python",
"repo": "thdb-theo/Board-Games",
"path": "/NaC/difs/dif1.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> = [i for i, x in enumerate(common_board) if not x]
return random.choice(available_moves)<|fim_prefix|># repo: thdb-theo/Board-Games path: /NaC/difs/dif1.py
import random
def completely_random(*args):
<|fim_middle|> common_board = args[0]
available_moves | code_fim | easy | {
"lang": "python",
"repo": "thdb-theo/Board-Games",
"path": "/NaC/difs/dif1.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Act
runner.invoke(post_upcoming_cfps)
# Assert
assert patched_slack.mock.call_count == 2
@pytest.mark.unit
@pytest.mark.vcr
def test_post_no_open_cfps_found():
"""When there are no open CFPs, let the user know)"""
# Act
result = OpenCFPPost._generate_conference_text(confer... | code_fim | hard | {
"lang": "python",
"repo": "busy-beaver-dev/busy-beaver",
"path": "/tests/apps/call_for_proposals/test_cli.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Assert
assert patched_slack.mock.call_count == 2
@pytest.mark.unit
@pytest.mark.vcr
def test_post_no_open_cfps_found():
"""When there are no open CFPs, let the user know)"""
# Act
result = OpenCFPPost._generate_conference_text(conference_cfps=[])
# Assert
assert "No upcomi... | code_fim | hard | {
"lang": "python",
"repo": "busy-beaver-dev/busy-beaver",
"path": "/tests/apps/call_for_proposals/test_cli.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: busy-beaver-dev/busy-beaver path: /tests/apps/call_for_proposals/test_cli.py
import pytest
from busy_beaver.apps.call_for_proposals.cli import OpenCFPPost, post_upcoming_cfps
from tests._utilities import FakeSlackClient
MODULE_TO_TEST = "busy_beaver.apps.call_for_proposals.cli"
@pytest.fixtur... | code_fim | hard | {
"lang": "python",
"repo": "busy-beaver-dev/busy-beaver",
"path": "/tests/apps/call_for_proposals/test_cli.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: meqash/mcmcplot path: /mcmcplot/mcseaborn.py
#!/usr/bin/env python2
# -*- coding: utf-8 -*-
"""
Created on August 5, 2018
@author: prmiles
"""
import pandas as pd
import seaborn as sns
from .utilities import generate_names, check_settings
from .utilities import setup_subsample
def plot_joint_... | code_fim | hard | {
"lang": "python",
"repo": "meqash/mcmcplot",
"path": "/mcmcplot/mcseaborn.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> Kwargs:
* **names** (:py:class:`list`): List of strings - name \
of each parameter. Default: `None`
* **settings** (:py:class:`dict`): Settings for features \
of this method. Default: `None`
* **index** (:py:class:`list`): Category for each row of \
chai... | code_fim | hard | {
"lang": "python",
"repo": "meqash/mcmcplot",
"path": "/mcmcplot/mcseaborn.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
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