text stringlengths 232 16.3k | domain stringclasses 1
value | difficulty stringclasses 3
values | meta dict |
|---|---|---|---|
<|fim_prefix|># repo: JmfanBU/F1tenth_BU path: /racecar_simulator/racecar_control/scripts/keyboard_teleop.py
#!/usr/bin/env python
import rospy
from racecar_control.msg import drive_param
import curses
forward = 0;
left = 0;
stdscr = curses.initscr()
curses.cbreak()
stdscr.keypad(1)
rospy.init_node('keyop', anonym... | code_fim | medium | {
"lang": "python",
"repo": "JmfanBU/F1tenth_BU",
"path": "/racecar_simulator/racecar_control/scripts/keyboard_teleop.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> key = stdscr.getch()
stdscr.refresh()
if key == curses.KEY_UP:
forward = forward + 1;
if forward >= 40:
forward = 40
elif forward < -40:
forward = -40
stdscr.addstr(2, 20, "Up ")
stdscr.addstr(2, 25, '%.2f' % forward)
stdscr.addstr(5, 20, " ")
elif key == curses.KEY_DOWN:
for... | code_fim | medium | {
"lang": "python",
"repo": "JmfanBU/F1tenth_BU",
"path": "/racecar_simulator/racecar_control/scripts/keyboard_teleop.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mua2010/CS589 path: /_hw3/hw3/Submission/Code/Template_Stacking.py
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
from sklearn.ensemble import (
StackingClassifier,
RandomForestClassifier
)
import pandas as pd
from sklearn.metrics import f1_score
# feel free ... | code_fim | medium | {
"lang": "python",
"repo": "mua2010/CS589",
"path": "/_hw3/hw3/Submission/Code/Template_Stacking.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> np.random.seed(0)
train_X, train_y, test_X, test_y = load_data()
# Stacking models:
# Create your stacked model using StackingClassifier
base_models = [
('rfc', RandomForestClassifier()),
('svm', SVC()),
('gnb', GaussianNB()),
('knc', KNeighborsClas... | code_fim | medium | {
"lang": "python",
"repo": "mua2010/CS589",
"path": "/_hw3/hw3/Submission/Code/Template_Stacking.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> return train_X, train_y, test_X, test_y
def main():
np.random.seed(0)
train_X, train_y, test_X, test_y = load_data()
# Stacking models:
# Create your stacked model using StackingClassifier
base_models = [
('rfc', RandomForestClassifier()),
('svm', SVC()),
... | code_fim | medium | {
"lang": "python",
"repo": "mua2010/CS589",
"path": "/_hw3/hw3/Submission/Code/Template_Stacking.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> re1 = ""
re2 = ""
for i in range(len(A)):
if A[i] != B[i]:
re1 += A[i]
re2 += B[i]
if len(re1) == len(re2) == 2 and re1 == re2[::-1]:
return True
return False<|fim_prefix|># repo: Ep... | code_fim | hard | {
"lang": "python",
"repo": "EpsilonHF/Leetcode",
"path": "/Python/859.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: EpsilonHF/Leetcode path: /Python/859.py
"""
Given two strings A and B of lowercase letters, return true
if and only if we can swap two letters in A so that the result
equals B.
Example 1:
<|fim_suffix|> if A == B and len(A) > len(set(A)):
return True
... | code_fim | hard | {
"lang": "python",
"repo": "EpsilonHF/Leetcode",
"path": "/Python/859.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Mindik/project1 path: /helpers.py
#This is a file from CS50 Finance
from functools import wraps
from flask import redirect, render_template, session
from threading import Thread
from flask_mail import Message
from application import app, mail
ALLOWED_EXTENSIONS = {"png", "PNG", "jpg", "jpeg", "... | code_fim | hard | {
"lang": "python",
"repo": "Mindik/project1",
"path": "/helpers.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>def send_mail(subject, recipient, template, **kwargs):
msg = Message(subject, recipients=[recipient])
msg.html = render_template(template, **kwargs)
thr = Thread(target=async_send_mail, args=[app, msg])
thr.start()
return thr<|fim_prefix|># repo: Mindik/project1 path: /helpers.py
#Thi... | code_fim | hard | {
"lang": "python",
"repo": "Mindik/project1",
"path": "/helpers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> Disabled = "disabled",
Basic = "basic",
Enhanced = "enhanced",
UnknownFutureValue = "unknownFutureValue",<|fim_prefix|># repo: microsoftgraph/msgraph-sdk-python path: /msgraph/generated/models/image_tagging_choice.py
from enum import Enum
<|fim_middle|>class ImageTaggingChoice(str, Enum)... | code_fim | easy | {
"lang": "python",
"repo": "microsoftgraph/msgraph-sdk-python",
"path": "/msgraph/generated/models/image_tagging_choice.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: microsoftgraph/msgraph-sdk-python path: /msgraph/generated/models/image_tagging_choice.py
from enum import Enum
<|fim_suffix|> Disabled = "disabled",
Basic = "basic",
Enhanced = "enhanced",
UnknownFutureValue = "unknownFutureValue",<|fim_middle|>class ImageTaggingChoice(str, Enum)... | code_fim | easy | {
"lang": "python",
"repo": "microsoftgraph/msgraph-sdk-python",
"path": "/msgraph/generated/models/image_tagging_choice.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> verbose_name = _('cigarette')
verbose_name_plural = _('cigarettes')
def __unicode__(self):
return u'%s' % ( self.pk)
def get_cigarette_user_id(self):
"Returns the user id who smoked the cigarette"
return self.cigarette_user.pk
def get_date(self):
... | code_fim | hard | {
"lang": "python",
"repo": "d-t/quitbit",
"path": "/quitbit/apps/qb_main/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> "Returns the id of the parent comment"
return self.parent_comment.pk
def set_parent_comment(parent_comment):
self.starting_comment = parent_comment
# Entity Cigarette
class Cigarette(models.Model):
"""
Cigarette smoked by a user
"""
# User - Foreign key
... | code_fim | hard | {
"lang": "python",
"repo": "d-t/quitbit",
"path": "/quitbit/apps/qb_main/models.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: d-t/quitbit path: /quitbit/apps/qb_main/models.py
from django.db import models
from django.conf import settings
from django.utils.translation import ugettext_lazy as _
from model_utils.models import TimeStampedModel
user = settings.AUTH_USER_MODEL
commment_lenght = settings.COMMENT_LENGTH
# En... | code_fim | hard | {
"lang": "python",
"repo": "d-t/quitbit",
"path": "/quitbit/apps/qb_main/models.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: SlapOS/slapos.core path: /master/bt5/slapos_accounting/SkinTemplateItem/portal_skins/slapos_consumption/ComputeNode_reportComputeNodeConsumption.py
from zExceptions import Unauthorized
if REQUEST is not None:
raise Unauthorized
portal = context.getPortalObject()
compute_node = context
<|fim_s... | code_fim | hard | {
"lang": "python",
"repo": "SlapOS/slapos.core",
"path": "/master/bt5/slapos_accounting/SkinTemplateItem/portal_skins/slapos_consumption/ComputeNode_reportComputeNodeConsumption.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>reference = "TIOCONS-%s-%s" % (compute_node.getReference(), source_reference)
version = "%s" % context.getPortalObject().portal_ids.generateNewId(
id_group=('slap_tioxml_consumption_reference', reference), default=1)
document = portal.consumption_document_module.newContent(
portal_type="Computer Cons... | code_fim | medium | {
"lang": "python",
"repo": "SlapOS/slapos.core",
"path": "/master/bt5/slapos_accounting/SkinTemplateItem/portal_skins/slapos_consumption/ComputeNode_reportComputeNodeConsumption.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Robbie-Cook/cosc470Assignment2 path: /test_conflict-20180923-152216.py
import numpy as np
# Read in training data and labels
# Some useful parsing functions
# male/female -> 0/1
def parseSexLabel(string):
if (string.startswith('male')):
return 0
if (string.startswith('female'))... | code_fim | hard | {
"lang": "python",
"repo": "Robbie-Cook/cosc470Assignment2",
"path": "/test_conflict-20180923-152216.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
'''Trains a simple convnet on the MNIST dataset.
Gets to 99.25% test accuracy after 12 epochs
(there is still a lot of margin for parameter tuning).
16 seconds per epoch on a GRID K520 GPU.
'''
import tensorflow as tf
from tensorflow import keras
batch_size = 128
epochs = 12
x_train = trainingFaces
y_... | code_fim | hard | {
"lang": "python",
"repo": "Robbie-Cook/cosc470Assignment2",
"path": "/test_conflict-20180923-152216.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: C-Wood4357/AdventOfCode2020 path: /10/10-2.py
from functools import reduce
with open("input.txt") as f:
numbers = f.read().split("\n")
n = sorted(list(map(lambda x: int(x), numbers)))
n.insert(0, 0)
n.append(n[-1] + 3)
target = n[-1]
memoize = {}
<|fim_suffix|> if number == target:
... | code_fim | medium | {
"lang": "python",
"repo": "C-Wood4357/AdventOfCode2020",
"path": "/10/10-2.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if number == target:
return 1
if number in memoize.keys():
return memoize[number]
paths = 0
if number + 1 in n:
paths += part2(number + 1)
if number + 2 in n:
paths += part2(number + 2)
if number + 3 in n:
paths += part2(number + 3)
memoi... | code_fim | medium | {
"lang": "python",
"repo": "C-Wood4357/AdventOfCode2020",
"path": "/10/10-2.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>if __name__ == '__main__':
# 图像加载
image = Image.open('../datas/xiaoren.png')
# 图像转换为numpy数组
img = np.asarray(image)
print(img.shape)
# 构建一个新的图像
imageNew = np.zeros((600,100,3))
imageNew = imageNew.astype(np.uint8)
misc.imsave('m.png',imageNew)<|fim_prefix|># repo: zlwm... | code_fim | medium | {
"lang": "python",
"repo": "zlwmzh/pythonLearn",
"path": "/ai_sklearn/0626/01_压缩相关知识.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # 构建一个新的图像
imageNew = np.zeros((600,100,3))
imageNew = imageNew.astype(np.uint8)
misc.imsave('m.png',imageNew)<|fim_prefix|># repo: zlwmzh/pythonLearn path: /ai_sklearn/0626/01_压缩相关知识.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/6/26 16:11
# @Author : Micky
# @Site ... | code_fim | hard | {
"lang": "python",
"repo": "zlwmzh/pythonLearn",
"path": "/ai_sklearn/0626/01_压缩相关知识.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: zlwmzh/pythonLearn path: /ai_sklearn/0626/01_压缩相关知识.py
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Time : 2019/6/26 16:11
# @Author : Micky
# @Site :
# @File : 01_压缩相关知识.py
# @Software: PyCharm
<|fim_suffix|> # 构建一个新的图像
imageNew = np.zeros((600,100,3))
imageNew = image... | code_fim | hard | {
"lang": "python",
"repo": "zlwmzh/pythonLearn",
"path": "/ai_sklearn/0626/01_压缩相关知识.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> operations = [
migrations.RemoveField(
model_name='optionvoting',
name='totalVotes',
),
migrations.AddField(
model_name='mcqoption',
name='totalVotes',
field=models.IntegerField(default=0),
),
]<|fim_prefix... | code_fim | medium | {
"lang": "python",
"repo": "almahdiy/IT_PDP_Conference",
"path": "/API/PDPAPI/migrations/0012_auto_20181105_1200.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: almahdiy/IT_PDP_Conference path: /API/PDPAPI/migrations/0012_auto_20181105_1200.py
# Generated by Django 2.1.2 on 2018-11-05 12:00
from django.db import migrations, models
class Migration(migrations.Migration):
<|fim_suffix|> operations = [
migrations.RemoveField(
model... | code_fim | medium | {
"lang": "python",
"repo": "almahdiy/IT_PDP_Conference",
"path": "/API/PDPAPI/migrations/0012_auto_20181105_1200.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Chad-Mowbray/FAQ_Creator path: /components/bases/DataFrameCreatorBase.py
import sys
import pandas as pd
from components.helpers.Logger import Logger
class DataFrameCreatorBase:
"""
DataFrameCreatorBase
"""
START_DATE = "03/16/2020"
def __init__(self, input_file):
<|fim_su... | code_fim | medium | {
"lang": "python",
"repo": "Chad-Mowbray/FAQ_Creator",
"path": "/components/bases/DataFrameCreatorBase.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> self._input_file = input_file
self.df = self._read_raw_csv()
self._clean_df()
def __new__(cls, *args, **kwargs):
if not hasattr(cls, 'instance'):
cls.instance = super().__new__(cls)
return cls.instance
def _read_raw_csv(self):
try:
... | code_fim | medium | {
"lang": "python",
"repo": "Chad-Mowbray/FAQ_Creator",
"path": "/components/bases/DataFrameCreatorBase.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> if users.get_current_user():
url = users.create_logout_url(self.request.uri)
linktext = 'Logout'
user = users.get_current_user()
else:
url = users.create_login_url(self.request.uri)
linktext = 'Login'
user = "Anonymous... | code_fim | medium | {
"lang": "python",
"repo": "osantana-archive/fisllive",
"path": "/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> content = self.request.get('content')
if content:
message = Message()
if users.get_current_user():
message.author = users.get_current_user()
message.content = self.request.get('content')
message.put()
self.redirect("/"... | code_fim | medium | {
"lang": "python",
"repo": "osantana-archive/fisllive",
"path": "/main.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: osantana-archive/fisllive path: /main.py
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
#
# FISL Live
# =========
# Copyright (c) 2010, Triveos Tecnologia Ltda.
# License: AGPLv3
from os.path import *
from datetime import datetime
from google.appengine.api import users
from google.appengine.ex... | code_fim | hard | {
"lang": "python",
"repo": "osantana-archive/fisllive",
"path": "/main.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>d model ...')
# model = load_model(settings.MODEL_PATH)
# model = Model(inputs=model.input, outputs=model.get_layer('dnsthree').output)
# print('load done.')<|fim_prefix|># repo: Lionen/voiceprint-web path: /voiceprint/__init__.py
import pymysql
pymysql.install_as_MySQLdb()
# from keras.models import l... | code_fim | medium | {
"lang": "python",
"repo": "Lionen/voiceprint-web",
"path": "/voiceprint/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Lionen/voiceprint-web path: /voiceprint/__init__.py
import pymysql
pymysql.install_as_MySQLdb()
# from keras.models import load_<|fim_suffix|>d model ...')
# model = load_model(settings.MODEL_PATH)
# model = Model(inputs=model.input, outputs=model.get_layer('dnsthree').output)
# print('load don... | code_fim | medium | {
"lang": "python",
"repo": "Lionen/voiceprint-web",
"path": "/voiceprint/__init__.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>=model.input, outputs=model.get_layer('dnsthree').output)
# print('load done.')<|fim_prefix|># repo: Lionen/voiceprint-web path: /voiceprint/__init__.py
import pymysql
pymysql.install_as_MySQLdb()
# from keras.models import load_model
# from keras.models import Model
# from ai import settings
#
# print... | code_fim | medium | {
"lang": "python",
"repo": "Lionen/voiceprint-web",
"path": "/voiceprint/__init__.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> ins = self.film_table.insert().values(item)
try:
self.conn.execute(ins)
except Exception, e:
pass
return item
def close_spider(self, spider):
self.conn.close()<|fim_prefix|># repo: fentensoft/douban-film-spider path: /douban/pipelines.... | code_fim | medium | {
"lang": "python",
"repo": "fentensoft/douban-film-spider",
"path": "/douban/pipelines.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: fentensoft/douban-film-spider path: /douban/pipelines.py
# -*- coding: utf-8 -*-
# Define your item pipelines here
#
# Don't forget to add your pipeline to the ITEM_PIPELINES setting
# See: http://doc.scrapy.org/en/latest/topics/item-pipeline.html
from sqlalchemy import create_engine, MetaData, ... | code_fim | medium | {
"lang": "python",
"repo": "fentensoft/douban-film-spider",
"path": "/douban/pipelines.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def process_item(self, item, spider):
ins = self.film_table.insert().values(item)
try:
self.conn.execute(ins)
except Exception, e:
pass
return item
def close_spider(self, spider):
self.conn.close()<|fim_prefix|># repo: fentensoft/do... | code_fim | hard | {
"lang": "python",
"repo": "fentensoft/douban-film-spider",
"path": "/douban/pipelines.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> correspond = pd.read_csv(file_, sep=',', header='infer')
mail = pd.merge(correspond, mailTypes, how='left', left_on=['correspondenceTypeId'], right_on=['typeId'])
mail.drop('typeId', axis=1, inplace=True)
mail.columns = ['projectId', 'correspondenceId', 'sentDate', 'fromOrganizationId', 'f... | code_fim | hard | {
"lang": "python",
"repo": "lexxmachina/aconex-job-applicaton",
"path": "/mailMerge.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: lexxmachina/aconex-job-applicaton path: /mailMerge.py
#!/usr/bin/python
import glob
import pandas as pd
import numpy as np
manifest = pd.read_csv('./manifest.csv', sep=',', names=['projectId','records'], skiprows=[0])
mailTypes = pd.read_csv('./mail_types.csv', sep=',', names=['typeId','typeNa... | code_fim | hard | {
"lang": "python",
"repo": "lexxmachina/aconex-job-applicaton",
"path": "/mailMerge.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>us = "/get_status"
create_order = "/create_order"
ask_store = "/ask_store"
check = "/check"
test = "/test"<|fim_prefix|># repo: VladPyzh/PythonClientServer path: /ServerClient2020/handlers.py
class Handlers():
change_store = "/change_store"
cha<|fim_middle|>nge_status = "/change_s... | code_fim | medium | {
"lang": "python",
"repo": "VladPyzh/PythonClientServer",
"path": "/ServerClient2020/handlers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VladPyzh/PythonClientServer path: /ServerClient2020/handlers.py
class Handlers():
change_store = "/change_store"
cha<|fim_suffix|>_store = "/ask_store"
check = "/check"
test = "/test"<|fim_middle|>nge_status = "/change_status"
mail = "/mail"
get_status = "/get_status"
... | code_fim | medium | {
"lang": "python",
"repo": "VladPyzh/PythonClientServer",
"path": "/ServerClient2020/handlers.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: NCAR/SoftFlow path: /lib/python/folding_findline.py
# dg_kernel plots
import os
import re
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import csv
import sys
NE_SIZE = 128
TITLE_SIZE = 35
TEXT_SIZE = 30
MARKER_SIZE = 10
LINE_WIDTH = 5
colors = { idx:cn... | code_fim | hard | {
"lang": "python",
"repo": "NCAR/SoftFlow",
"path": "/lib/python/folding_findline.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>nonpeaks_avgsum = sum(nonpeak1['Average']) + sum(nonpeak2['Average'])
nonpeaks_normavg = {}
for i, line in enumerate(nonpeak1['Head']):
if nonpeaks_normavg.has_key(line):
nonpeaks_normavg[line] += nonpeak1['Average'][i]
else:
nonpeaks_normavg[line] = nonpeak1['Average'][i]
for i,... | code_fim | hard | {
"lang": "python",
"repo": "NCAR/SoftFlow",
"path": "/lib/python/folding_findline.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
nonpeak1 = read_histogram('%s/%s_low_linelevel%d_region0.csv'%(ROOT, eventname, callstacklevel))
nonpeak2 = read_histogram('%s/%s_low_linelevel%d_region1.csv'%(ROOT, eventname, callstacklevel))
nonpeaks_avgsum = sum(nonpeak1['Average']) + sum(nonpeak2['Average'])
nonpeaks_normavg = {}
for i, line in e... | code_fim | hard | {
"lang": "python",
"repo": "NCAR/SoftFlow",
"path": "/lib/python/folding_findline.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Compute IK solution
goal_curr = blue.inverse_kinematics(target_position, target_orientation)
# Send command to robot
if goal_curr != []:
goal = goal_curr
print("goal: ", goal)
blue.set_joint_positions(goal, d... | code_fim | hard | {
"lang": "python",
"repo": "yusukeurakami/blue_soylent",
"path": "/examples/leap_controller.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> # Pre-defined Initial position of the robot
target_angles = target_angles_init.copy()
# orientation
target_angles[0] += (ori[0]*1 + target_position[1]*1.5) # shoulder dir
target_angles[4] += ori[2] # arm twist
target_angles[5] ... | code_fim | hard | {
"lang": "python",
"repo": "yusukeurakami/blue_soylent",
"path": "/examples/leap_controller.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: yusukeurakami/blue_soylent path: /examples/leap_controller.py
#!/usr/bin/env python2
# A basic example of sending Blue a command in cartesian space.
from blue_interface import BlueInterface
import numpy as np
import time
import sys
import argparse
import Leap
from utils.rotations import quat2eu... | code_fim | hard | {
"lang": "python",
"repo": "yusukeurakami/blue_soylent",
"path": "/examples/leap_controller.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>#comparison
c=[2,3,4]
print(a==b)
print(a!=b)
#slice
a=[9,8,7,6,5,4]
print(a[0:3])
print(a[:4])
print(a[1:])
print(a[:])
print(a[2:2])
print(a[0:6:2])
print(a[0:6:3])
'''#a.apppend(element)
a=[1,2,3,4,5]
b=int(input('Enter number to append:'))
a.append(b)
print(a)
#insert(index,element)
a.insert(0,0)
p... | code_fim | medium | {
"lang": "python",
"repo": "YSreylin/HTML",
"path": "/1101901079/0012/list.example.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: YSreylin/HTML path: /1101901079/0012/list.example.py
#create a list
a = [2,3,4,5,6,7,8,9,10]
print(a)
#indexing
b = int(input('Enter indexing value:'))
print('The result is:',a[b])
print(a[8])
print(a[-1])
#slicing
print(a[0:3])
print(a[0:])
#conconteation
b=[20,30]
print(a+b)
#Repetition
pri... | code_fim | medium | {
"lang": "python",
"repo": "YSreylin/HTML",
"path": "/1101901079/0012/list.example.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>'''#a.apppend(element)
a=[1,2,3,4,5]
b=int(input('Enter number to append:'))
a.append(b)
print(a)
#insert(index,element)
a.insert(0,0)
print(a)
#a.extend(c)
c=[6,7,8,9]
a.extend(c)
print(a)
#one more'''<|fim_prefix|># repo: YSreylin/HTML path: /1101901079/0012/list.example.py
#create a list
a = [2,3,4,... | code_fim | medium | {
"lang": "python",
"repo": "YSreylin/HTML",
"path": "/1101901079/0012/list.example.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: clovisdanielcosta/python-dio path: /aula8_lambda_contador_letras.py
# As variáveis abaixo estão recebendo uma função <|fim_suffix|>, 'marreco']
print(contador_letras(lista_animais))<|fim_middle|>anônima
contador_letras = lambda lista: [len(x) for x in lista]
lista_animais = ['cachorro', 'pato' | code_fim | medium | {
"lang": "python",
"repo": "clovisdanielcosta/python-dio",
"path": "/aula8_lambda_contador_letras.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>r x in lista]
lista_animais = ['cachorro', 'pato', 'marreco']
print(contador_letras(lista_animais))<|fim_prefix|># repo: clovisdanielcosta/python-dio path: /aula8_lambda_contador_letras.py
# As variáveis abaixo estão recebendo uma função <|fim_middle|>anônima
contador_letras = lambda lista: [len(x) fo | code_fim | easy | {
"lang": "python",
"repo": "clovisdanielcosta/python-dio",
"path": "/aula8_lambda_contador_letras.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>z = DisaggregationManager._overlap_average(np.array(list(w)), stride=128)
print(z.shape)
print(x.shape)
assert z.shape == x.shape<|fim_prefix|># repo: dumorgan/projeto-progamacao-puc-rio path: /tests/test_overlap_average.py
from disaggregation import DisaggregationManager
import numpy as np
from more_ite... | code_fim | medium | {
"lang": "python",
"repo": "dumorgan/projeto-progamacao-puc-rio",
"path": "/tests/test_overlap_average.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dumorgan/projeto-progamacao-puc-rio path: /tests/test_overlap_average.py
from disaggregation import DisaggregationManager
import numpy as np
from more_itertools import windowed
<|fim_suffix|>z = DisaggregationManager._overlap_average(np.array(list(w)), stride=128)
print(z.shape)
print(x.shape)
a... | code_fim | medium | {
"lang": "python",
"repo": "dumorgan/projeto-progamacao-puc-rio",
"path": "/tests/test_overlap_average.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: stephr3/drf-films-and-theaters path: /films/serializers.py
from rest_framework import serializers
from films.models import *
from django.contrib.auth.models import User
class UserSerializer(serializers.ModelSerializer):
films = serializers.PrimaryKeyRelatedField(many=True, queryset=Film.obje... | code_fim | medium | {
"lang": "python",
"repo": "stephr3/drf-films-and-theaters",
"path": "/films/serializers.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> model = Theater
fields = ('id', 'name', 'city', 'films', 'owner')
depth = 1
class TheaterWriteSerializer(serializers.ModelSerializer):
class Meta:
model = Theater
fields = ('id', 'name', 'city')<|fim_prefix|># repo: stephr3/drf-films-and-theaters path: /films... | code_fim | hard | {
"lang": "python",
"repo": "stephr3/drf-films-and-theaters",
"path": "/films/serializers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> owner = serializers.ReadOnlyField(source='owner.username')
class Meta:
model = Film
fields = ('id', 'title', 'year_prod', 'genre', 'theater_set', 'owner')
depth = 1
class FilmWriteSerializer(serializers.ModelSerializer):
genre = serializers.PrimaryKeyRelatedField(quer... | code_fim | medium | {
"lang": "python",
"repo": "stephr3/drf-films-and-theaters",
"path": "/films/serializers.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hughshanahan/CS2900-Lab-1 path: /tester/utils/cp3.py
def check_orthogonal(u, v):
<|fim_suffix|> import inspect
import re
local_vars = inspect.currentframe().f_back.f_locals
return len(re.findall("p\\s*=\\s*0", str(local_vars))) == 0<|fim_middle|> return u.dot(v) == 0
def check... | code_fim | easy | {
"lang": "python",
"repo": "hughshanahan/CS2900-Lab-1",
"path": "/tester/utils/cp3.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> import inspect
import re
local_vars = inspect.currentframe().f_back.f_locals
return len(re.findall("p\\s*=\\s*0", str(local_vars))) == 0<|fim_prefix|># repo: hughshanahan/CS2900-Lab-1 path: /tester/utils/cp3.py
def check_orthogonal(u, v):
<|fim_middle|> return u.dot(v) == 0
def check... | code_fim | easy | {
"lang": "python",
"repo": "hughshanahan/CS2900-Lab-1",
"path": "/tester/utils/cp3.py",
"mode": "spm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: hughshanahan/CS2900-Lab-1 path: /tester/utils/cp3.py
def check_orthogonal(u, v):
<|fim_suffix|>def check_p():
import inspect
import re
local_vars = inspect.currentframe().f_back.f_locals
return len(re.findall("p\\s*=\\s*0", str(local_vars))) == 0<|fim_middle|> return u.dot(v) =... | code_fim | easy | {
"lang": "python",
"repo": "hughshanahan/CS2900-Lab-1",
"path": "/tester/utils/cp3.py",
"mode": "psm",
"license": "Apache-2.0",
"source": "the-stack-v2"
} |
<|fim_suffix|> for a in LangsMediaWiki:
#print a.shortName
indexPageName = 'Indeks:{0}_-_Związki_frazeologiczne'.format(a.upperName)
try: phraseList[a.shortName] = pywikibot.Page(site, indexPageName).get()
except pywikibot.NoPage:
phraseList['%s' % a.shortName] = ... | code_fim | hard | {
"lang": "python",
"repo": "alkamid/wiktionary",
"path": "/fraz.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>
for a in lista_stron:
try: word = Haslo(a)
except notFromMainNamespace:
continue
except sectionsNotFound:
continue
except WrongHeader:
continue
else:
if word.type == 3:
for lang in word.listLangs:
... | code_fim | hard | {
"lang": "python",
"repo": "alkamid/wiktionary",
"path": "/fraz.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: alkamid/wiktionary path: /fraz.py
#!/usr/bin/python
# -*- coding: utf-8 -*-
import pywikibot
from pywikibot import pagegenerators
import re
from pywikibot import xmlreader
import datetime
import collections
from klasa import *
def fraz(data):
data_slownie = data[6:8] + '.' + data[4:6] + '.... | code_fim | hard | {
"lang": "python",
"repo": "alkamid/wiktionary",
"path": "/fraz.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> return {}
def get_dialog(self):
config: dict = self.read_config()
if config.get("OS") == "Windows":
return WindowsDialog()
return WebDialog()
def render(self):
self.dialog.render()
if __name__ == "__main__":
app = Application()
app.re... | code_fim | medium | {
"lang": "python",
"repo": "duthaho/python-design-patterns",
"path": "/creational/factory.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: duthaho/python-design-patterns path: /creational/factory.py
from abc import abstractmethod
class BaseButton:
@abstractmethod
def render(self):
pass
@abstractmethod
def on_click(self):
pass
class WindowsButton(BaseButton):
def render(self):
print("R... | code_fim | medium | {
"lang": "python",
"repo": "duthaho/python-design-patterns",
"path": "/creational/factory.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def on_click(self):
print("On click")
class HtmlButton(BaseButton):
def render(self):
print("Render html button")
def on_click(self):
print("On click")
class BaseDialog:
@abstractmethod
def create_button(self) -> BaseButton:
pass
def render(sel... | code_fim | medium | {
"lang": "python",
"repo": "duthaho/python-design-patterns",
"path": "/creational/factory.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>realistic_v14-v1/240000/E2F9F049-5912-EA11-80CE-0017A4771048.root', '/store/mc/RunIIFall17MiniAODv2/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13TeV-madgraph-mcatnlo-pythia8/MINIAODSIM/PU2017_12Apr2018_rp_94X_mc2017_realistic_v14-v1/240000/34CB6C5F-5912-EA11-B919-0425C5DE7BF4.root', '/store/mc/RunIIFall... | code_fim | hard | {
"lang": "python",
"repo": "nistefan/RandomizedParametersSeparator",
"path": "/DarkMatterMap2017/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13TeV_madgraph_mcatnlo_pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_Mchi-55_Mphi-100_TuneCP5_13TeV-madgraph-mcatnlo-pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13... |
<|fim_prefix|># repo: nistefan/RandomizedParametersSeparator path: /DarkMatterMap2017/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13TeV_madgraph_mcatnlo_pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_Mchi-55_Mphi-100_TuneCP5_13TeV-madgraph-mcatnlo-pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13TeV_madgraph_mcat... | code_fim | hard | {
"lang": "python",
"repo": "nistefan/RandomizedParametersSeparator",
"path": "/DarkMatterMap2017/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13TeV_madgraph_mcatnlo_pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_Mchi-55_Mphi-100_TuneCP5_13TeV-madgraph-mcatnlo-pythia8/TTbarDMJets_Dilepton_pseudoscalar_LO_TuneCP5_13... |
<|fim_suffix|>if __name__ == "__main__":
print("1 Chỗ này hơi lâu bạn đợi tí")
phobert = AutoModel.from_pretrained("vinai/phobert-base")
print("2")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base", use_fast=False)
print("3")
predict("tôi làm giấy X ở đâu", phobert, tokenizer)
p... | code_fim | medium | {
"lang": "python",
"repo": "mariorenger/pB-classification",
"path": "/codepython/test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: mariorenger/pB-classification path: /codepython/test.py
from utils import *
from wordEmbedding import *
print("bat dau")
def predict(text, phobert, tokenizer):
<|fim_suffix|> print(y_predict+1)
if __name__ == "__main__":
print("1 Chỗ này hơi lâu bạn đợi tí")
phobert = AutoModel.from_... | code_fim | hard | {
"lang": "python",
"repo": "mariorenger/pB-classification",
"path": "/codepython/test.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> print(y_predict+1)
if __name__ == "__main__":
print("1 Chỗ này hơi lâu bạn đợi tí")
phobert = AutoModel.from_pretrained("vinai/phobert-base")
print("2")
tokenizer = AutoTokenizer.from_pretrained("vinai/phobert-base", use_fast=False)
print("3")
predict("tôi làm giấy X ở đâu", p... | code_fim | medium | {
"lang": "python",
"repo": "mariorenger/pB-classification",
"path": "/codepython/test.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ct-17/myblog path: /search/apps.py
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
<|fim_suffix|> name = 'search'
verbose_name = _("Search")<|fim_middle|>class SearchConfig(AppConfig):
| code_fim | easy | {
"lang": "python",
"repo": "ct-17/myblog",
"path": "/search/apps.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> name = 'search'
verbose_name = _("Search")<|fim_prefix|># repo: ct-17/myblog path: /search/apps.py
from django.apps import AppConfig
from django.utils.translation import gettext_lazy as _
<|fim_middle|>class SearchConfig(AppConfig):
| code_fim | easy | {
"lang": "python",
"repo": "ct-17/myblog",
"path": "/search/apps.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: Gajasurve/fb-mosaic path: /fb-mosaic.py
import os
from PIL import Image
import urllib
import json
import math
def download_images(a,b):
image_count = 0
k = a
no_of_images = b
baseURL='https://graph.facebook.com/v2.2/'
imgURL='/picture?type=large'
sil_check='/picture?redirect=false'
while ... | code_fim | hard | {
"lang": "python",
"repo": "Gajasurve/fb-mosaic",
"path": "/fb-mosaic.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>a = int(raw_input('Enter the fb-id from where to begin:'))
b = int(raw_input('Enter the number of images to download (a square):'))
download_images(a,b)
resize_images()
create_mosaic(b)<|fim_prefix|># repo: Gajasurve/fb-mosaic path: /fb-mosaic.py
import os
from PIL import Image
import urllib
import json
... | code_fim | hard | {
"lang": "python",
"repo": "Gajasurve/fb-mosaic",
"path": "/fb-mosaic.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>.content
df_list = pandas.read_html(html)
# Pull relevant URLs<|fim_prefix|># repo: tdanzey/Ravine-Rumble path: /Build ELO Dataset/01a_Scrape League URLs from Yahoo.py
# Import packages
import pandas
import requests
import lxml
# Get page content
url = "https://archive.fantasysports.yahoo.com<|fim_midd... | code_fim | medium | {
"lang": "python",
"repo": "tdanzey/Ravine-Rumble",
"path": "/Build ELO Dataset/01a_Scrape League URLs from Yahoo.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: tdanzey/Ravine-Rumble path: /Build ELO Dataset/01a_Scrape League URLs from Yahoo.py
# Import packages
import pandas
import requests
import lxml
# <|fim_suffix|>/nfl/2017/189499?lhst=sched#lhstsched"
html = requests.get(url).content
df_list = pandas.read_html(html)
# Pull relevant URLs<|fim_midd... | code_fim | medium | {
"lang": "python",
"repo": "tdanzey/Ravine-Rumble",
"path": "/Build ELO Dataset/01a_Scrape League URLs from Yahoo.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> '''
Verifica que los valores de la columna rangoatrasohoras
sean los indicados
'''
def test_that_all_ranges_are_present(self):
df = get_clean_data()
RANGOS=['cancelled', '0-1.5', '1.5-3.5' ,'3.5-']
self.assertCategoricalLevelsEqual(list(df.toPandas()["rangoatrasohoras"].unique()),... | code_fim | medium | {
"lang": "python",
"repo": "rluiseugenio/dpa_rita",
"path": "/src/orquestadores/tasks/testing/test_clean_rangos.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> df = get_clean_data()
RANGOS=['cancelled', '0-1.5', '1.5-3.5' ,'3.5-']
self.assertCategoricalLevelsEqual(list(df.toPandas()["rangoatrasohoras"].unique()), RANGOS)<|fim_prefix|># repo: rluiseugenio/dpa_rita path: /src/orquestadores/tasks/testing/test_clean_rangos.py
#python -m marbles test_clean_ran... | code_fim | medium | {
"lang": "python",
"repo": "rluiseugenio/dpa_rita",
"path": "/src/orquestadores/tasks/testing/test_clean_rangos.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: rluiseugenio/dpa_rita path: /src/orquestadores/tasks/testing/test_clean_rangos.py
#python -m marbles test_clean_rangos.py
import unittest
from marbles.mixins import mixins
import pandas as pd
import requests
from pyspark.sql import SparkSession
import psycopg2 as pg
import pandas as pd
from pysp... | code_fim | medium | {
"lang": "python",
"repo": "rluiseugenio/dpa_rita",
"path": "/src/orquestadores/tasks/testing/test_clean_rangos.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ollavrova/pdf_crawler_rest_api path: /pdf_crawler/tests.py
from django.test import TestCase, Client
from pdf_crawler.models import Document
from rest_framework.reverse import reverse
class TestCase(TestCase):
client = Client()
<|fim_suffix|> Document.objects.create(name='First').sa... | code_fim | medium | {
"lang": "python",
"repo": "ollavrova/pdf_crawler_rest_api",
"path": "/pdf_crawler/tests.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> def test_endpoints(self):
"""
test for endpoints
"""
self.assertEqual(self.client.get(reverse('pdf_crawler:document-list')).status_code, 200)
self.assertEqual(self.client.get(reverse('pdf_crawler:document-detail', kwargs={'pk': 1})).status_code, 200)
se... | code_fim | medium | {
"lang": "python",
"repo": "ollavrova/pdf_crawler_rest_api",
"path": "/pdf_crawler/tests.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: betty29/code-1 path: /recipes/Python/578631_Extended_Euclidean_Algorithm/recipe-578631.py
# Author: Sam Erickson
# Date: 2/23/2016
#
# Program Description: This program gives the integer coefficients x,y to the
# equation ax+by=gcd(a,b) given by the extended Euclidean Algorithm.
<|fim_suffix|> ... | code_fim | hard | {
"lang": "python",
"repo": "betty29/code-1",
"path": "/recipes/Python/578631_Extended_Euclidean_Algorithm/recipe-578631.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> """
Preconditions - a and b are both positive integers.
Posconditions - The equation for ax+by=gcd(a,b) has been returned where
x and y are solved.
Input - a : int, b : int
Output - ax+by=gcd(a,b) : string
"""
b,a=max(a,b),min(a,b)
# Format of euclidList... | code_fim | medium | {
"lang": "python",
"repo": "betty29/code-1",
"path": "/recipes/Python/578631_Extended_Euclidean_Algorithm/recipe-578631.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>def _temp_attn(h, e_out_W, e_out, score_sum, time):
score = tf.squeeze(tf.matmul(tf.expand_dims(h, 1), e_out_W, transpose_b=True), [1])
score = tf.cond(time > 0, lambda: tf.exp(score)/(score_sum+1e-12), lambda: tf.exp(score))
a = _simple_norm(score)
ctx = tf.squeeze(tf.matmul(tf.expand_dim... | code_fim | hard | {
"lang": "python",
"repo": "VD44/RNN-Components",
"path": "/rnn_seq2seq.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: VD44/RNN-Components path: /rnn_seq2seq.py
import tensorflow as tf
from rnn_cells import gru_cell, lstm_cell
from tensorflow.python.ops import rnn
def shape_list(x):
ps = x.get_shape().as_list()
ts = tf.shape(x)
return [ts[i] if ps[i] is None else ps[i] for i in range(len(ps))]
def b... | code_fim | hard | {
"lang": "python",
"repo": "VD44/RNN-Components",
"path": "/rnn_seq2seq.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|>ws.logout, name='logout'),
path('test_auth', views.test, name='test'),
url(r'^activate/(?P<uidb64>[0-9A-Za-z_\-]+)/(?P<token>[0-9A-Za-z]{1,13}-[0-9A-Za-z]{1,20})/$',
views.activate, name='activate'),
path('change_user_status/<int:user_id>/<int:status>', views.change_user_status, name='... | code_fim | hard | {
"lang": "python",
"repo": "ahmadalwareh/SurveysBuilder",
"path": "/Accounts/urls.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: ahmadalwareh/SurveysBuilder path: /Accounts/urls.py
from django.conf.urls import url
from django.urls import path
from . import views
app_name = 'Accounts'
urlpatterns = [
path('update_info', views.update_info, name='update_info'),
path('create_user', views.create_u<|fim_suffix|>'change... | code_fim | hard | {
"lang": "python",
"repo": "ahmadalwareh/SurveysBuilder",
"path": "/Accounts/urls.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> src_port=int(UDP_header[0].hex(),16)
print("src_port:",src_port)
dst_port=int(UDP_header[1].hex(),16)
print("dst_port:",dst_port)
leng=int(UDP_header[2].hex(),16)
print("leng:",leng)
header_checksum=UDP_header[3].hex()
print("header_checksum:0x",header_checksum)
recv_... | code_fim | hard | {
"lang": "python",
"repo": "cnu-cse-datacom/2-packetcapture-HyunSongKwon",
"path": "/DC02_02_201701198_kwonhyunsong.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> seq_num=TCP_header[2]
print("seq_num:",seq_num)
ack_num=TCP_header[3]
print("ack_num:",ack_num)
header_len=(int(TCP_header[4].hex(),16)>>12)&0x000f
print("header_len:",header_len)
flags=int(TCP_header[4].hex(),16)&0x0fff
print("flags:",flags)
reserved=flags>>9
p... | code_fim | hard | {
"lang": "python",
"repo": "cnu-cse-datacom/2-packetcapture-HyunSongKwon",
"path": "/DC02_02_201701198_kwonhyunsong.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: cnu-cse-datacom/2-packetcapture-HyunSongKwon path: /DC02_02_201701198_kwonhyunsong.py
import socket
import struct
def parsing_ethernet_header(data):
ethernet_header=struct.unpack("!6c6c2s",data)
ether_dest = convert_ethernet_address(ethernet_header[0:6])
ether_src = convert_ethernet_... | code_fim | hard | {
"lang": "python",
"repo": "cnu-cse-datacom/2-packetcapture-HyunSongKwon",
"path": "/DC02_02_201701198_kwonhyunsong.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> #USED TO DECODE MAPQUEST RESPONSE
y = x.read().decode(encoding = 'utf-8')
print(y) # USE decoded response string to check with pretty json
#USED TO CONVERT DECODED STRING TO DICT/LISTS
z = json.loads(y) #dictionary of mapquest response which also includes lists
print(t... | code_fim | hard | {
"lang": "python",
"repo": "dblam/Duy-s-Python-Projects",
"path": "/PYTHON 32/Project 3/Module 3(sample).py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|>
if __name__ == '__main__':
#USED TO GET USER INPUTS
locationQ = tripQuantity()
locationList = quantityToLocations(locationQ) #print to double check
#CREATES A NEW SEARCH INSTANCE AND IT'S REQUEST URL
newSearch = Module1.URL()
newSearch.set_from_location(location... | code_fim | hard | {
"lang": "python",
"repo": "dblam/Duy-s-Python-Projects",
"path": "/PYTHON 32/Project 3/Module 3(sample).py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: dblam/Duy-s-Python-Projects path: /PYTHON 32/Project 3/Module 3(sample).py
# Duy B. Lam
# 61502602
# Project 3
# A module that reads the input and constructs the objects
# that will generate the program's output. This is the only
# module that should have an if __name__ == '__main__' block... | code_fim | hard | {
"lang": "python",
"repo": "dblam/Duy-s-Python-Projects",
"path": "/PYTHON 32/Project 3/Module 3(sample).py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> active_ids = context.get('active_ids',False)
supplier_ids = self.pool['ebiz.supplier.account.line'].create_ebiz_supplier_account_line(cr, uid, active_ids, context=context)
return {
'view_type': 'form',
'view_mode': 'tree',
'res_model'... | code_fim | medium | {
"lang": "python",
"repo": "luohuayong/addons8",
"path": "/bysun_supplier_account/wizard/ebiz_supplier_account_create.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: luohuayong/addons8 path: /bysun_supplier_account/wizard/ebiz_supplier_account_create.py
# -*- coding: utf-8 -*- #
import time
from openerp.osv import osv, fields
import logging
import openerp.addons.decimal_precision as dp
<|fim_suffix|>class ebiz_supplier_account_create(osv.osv_memory):
_na... | code_fim | medium | {
"lang": "python",
"repo": "luohuayong/addons8",
"path": "/bysun_supplier_account/wizard/ebiz_supplier_account_create.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: LouisNUST/HumanPose3D-Pytorch path: /apps/hpn/get_video_stream.py
import sys, os
import cv2
# set the video reader
video_path = 0 # camera number index
# video_path = "/home/pacific/Documents/Work/Projects/Workflows/server/PycharmProjects/Pacific_AvatarGame_Host/humanpose_2d/LiveCamera/test.mp4... | code_fim | hard | {
"lang": "python",
"repo": "LouisNUST/HumanPose3D-Pytorch",
"path": "/apps/hpn/get_video_stream.py",
"mode": "psm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_suffix|> # save the video frame
videoWriter.write(frame)
cv2.waitKey(20) # wait 20 ms for next frame of the live video
# check whether manual exit command entered
if cv2.waitKey(1) & 0xFF == ord('q'):
break
else:
continue
videoReader.release... | code_fim | hard | {
"lang": "python",
"repo": "LouisNUST/HumanPose3D-Pytorch",
"path": "/apps/hpn/get_video_stream.py",
"mode": "spm",
"license": "MIT",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: thevindur/Python-Basics path: /w1790135 - ICT/Q3A.py
x=input("Do you really want to run this program? (y/n) : ")
x=x.upper()
if x=="Y" or x=="N" or x=="Q":
while x=="Y" or x=="N" or x=="Q":
if x=="Q":
print("Exiting the Program")
import sys
sys.exi... | code_fim | medium | {
"lang": "python",
"repo": "thevindur/Python-Basics",
"path": "/w1790135 - ICT/Q3A.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_suffix|> #You can run the program.Enter the code required to run the program
else:
print("Invalid selection is entered")<|fim_prefix|># repo: thevindur/Python-Basics path: /w1790135 - ICT/Q3A.py
x=input("Do you really want to run this program? (y/n) : ")
x=x.upper()
if x=="Y" or x=="N" or x=="Q":
... | code_fim | hard | {
"lang": "python",
"repo": "thevindur/Python-Basics",
"path": "/w1790135 - ICT/Q3A.py",
"mode": "spm",
"license": "unknown",
"source": "the-stack-v2"
} |
<|fim_prefix|># repo: JuliusC4esar/CS550 path: /looppractice.py
# Kai Joseph
# Loop Practice
# Since I worked on my own, I did not have to complete all 25 challenges (with Ms. Healey's permission). I completed a total of 14 challenges.
import sys
import random
''' 1.
Write a for loop that will print out all th... | code_fim | hard | {
"lang": "python",
"repo": "JuliusC4esar/CS550",
"path": "/looppractice.py",
"mode": "psm",
"license": "unknown",
"source": "the-stack-v2"
} |
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