code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
#!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Sat Jan 2 18:52:27 2021
@author: burak
"""
import veriler
import gorsel
import numpy as np
from sklearn.neighbors import KNeighborsClassifier
neighbors = np.arange(1,13)
train_accuracy = np.empty(len(neighbors))
test_accuracy = np.empty(len(neighbors))
... | normal | {
"blob_id": "133bd0b2affc3d29390edeab51299d294dafb709",
"index": 4188,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor n, k in enumerate(neighbors):\n knn = KNeighborsClassifier(n_neighbors=k, metric='minkowski')\n knn.fit(veriler.X_train, veriler.y_train.ravel())\n train_accuracy[n] = knn.sc... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('the original DNA sequence is', dnaSequence)
print('the first fragment is', firstFragment, 'and is', firstFragmentLen,
'letters long')
print('the second fragment is', secondFragment, 'and is', secondFragmentLen,
'let... | flexible | {
"blob_id": "7dc99d33023dbb13938ac413af7d3e9471fdbc3d",
"index": 126,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('the original DNA sequence is', dnaSequence)\nprint('the first fragment is', firstFragment, 'and is', firstFragmentLen,\n 'letters long')\nprint('the second fragment is', secondFr... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def main():
args = ['./input']
print('./input', end='')
for x in range(99):
print(' AA', end='')
args.append('AA')
print(args)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def main():
args = ['./input']
... | flexible | {
"blob_id": "9184779731d6102498934d77b6d3c0283fc594d9",
"index": 7498,
"step-1": "<mask token>\n\n\ndef main():\n args = ['./input']\n print('./input', end='')\n for x in range(99):\n print(' AA', end='')\n args.append('AA')\n print(args)\n\n\n<mask token>\n",
"step-2": "<mask token>\... | [
1,
2,
3,
4,
5
] |
L = "chaine de caractere"
print("parcours par élément")
for e in L :
print("caractere : *"+e+"*")
| normal | {
"blob_id": "cdc9bc97332a3914415b16f00bc098acc7a02863",
"index": 5020,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('parcours par élément')\nfor e in L:\n print('caractere : *' + e + '*')\n",
"step-3": "L = 'chaine de caractere'\nprint('parcours par élément')\nfor e in L:\n print('caracte... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class LinkedInGrabber(PageGrabber):
def get_info(self, email):
client = requests.Session()
print('[' + bc.CPRP + '?' + bc.CEND + '] ' + bc.CCYN + 'LinkedIn' +
bc.CEND)
HOMEPAGE_URL = 'htt... | flexible | {
"blob_id": "570e0d46aa1ea88d1784447e8f693199e3c3b6ad",
"index": 9488,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass LinkedInGrabber(PageGrabber):\n\n def get_info(self, email):\n client = requests.Session()\n print('[' + bc.CPRP + '?' + bc.CEND + '] ' + bc.CCYN + 'LinkedIn' +... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def start():
image_file = 'sample.png'
top_left_corner = [100, 100]
bottom_right_corner = [200, 200]
img = Image.open(image_file)
top_left_x = top_left_corner[0]
top_left_y = top_left_corner[1]
bottom... | flexible | {
"blob_id": "84476e1793242bf3bae51263c2db28ff555c25d7",
"index": 1104,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef start():\n image_file = 'sample.png'\n top_left_corner = [100, 100]\n bottom_right_corner = [200, 200]\n img = Image.open(image_file)\n top_left_x = top_left_corner... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Table(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class Price(models.Model):
price = models.FloatField(null=True)
class Marketdata(models.M... | flexible | {
"blob_id": "0054921928838d9aee63cf58f50a0a01ee12635d",
"index": 6049,
"step-1": "<mask token>\n\n\nclass Table(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass Price(models.Model):\n price = models.FloatField(null=True)\n\n\nclass Marketdata(... | [
5,
6,
10,
12,
14
] |
class Pwm:
def __init__(self, number, path, features):
self.id = number
self.path = path + 'pwm' + number
self.features = features
self.duty = self.get_feature('')
self.enable = self.get_feature('_enable')
<|reserved_special_token_0|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "c38aff77a7beebc13e7486150d549b876c830db8",
"index": 6104,
"step-1": "class Pwm:\n\n def __init__(self, number, path, features):\n self.id = number\n self.path = path + 'pwm' + number\n self.features = features\n self.duty = self.get_feature('')\n self.enable = s... | [
2,
3,
4,
5,
6
] |
import Libcplx as lc
# 1.Adición de vectores complejos
def adVector(v, w):
n = len(v)
r = []
for k in range(n):
r += [lc.cplxsum(v[k], w[k])]
return r
# 2.Inverso (aditivo) de un vector complejo
def invVector(v):
n = len(v)
r = []
for k in range(n):
r += [lc.cplxproduct((... | normal | {
"blob_id": "5f242ae801a239dde6a22e4fb68b4ef4b2459be6",
"index": 2599,
"step-1": "<mask token>\n\n\ndef adVector(v, w):\n n = len(v)\n r = []\n for k in range(n):\n r += [lc.cplxsum(v[k], w[k])]\n return r\n\n\n<mask token>\n\n\ndef MultEscalarVector(v, w):\n n = len(w)\n r = []\n for... | [
10,
13,
14,
15,
18
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if list_1 == list_1_rev:
print('You entered a polindrom!')
else:
print('Your string is not a polindrom')
<|reserved_special_token_1|>
list_1 = input('Enter something: ')
list_1_rev = list_1[::-1]
if list_1 == list_1_rev... | flexible | {
"blob_id": "45b56103db0a72ebbc7de340c4293e1f70552414",
"index": 5254,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif list_1 == list_1_rev:\n print('You entered a polindrom!')\nelse:\n print('Your string is not a polindrom')\n",
"step-3": "list_1 = input('Enter something: ')\nlist_1_rev = list... | [
0,
1,
2,
3
] |
# -*- coding: utf-8 -*-
import serial
import time
import argparse
def write_command(serial, comm, verbose = False, dt = None):
""" Encodes a command and sends it over the serial port """
if verbose and comm != "":
if dt is None:
print("{} \t\t-> {}".format(comm, serial.port... | normal | {
"blob_id": "3ffcab4b36c6ca05f1e667c628ebb873ebdc0d25",
"index": 7866,
"step-1": "<mask token>\n\n\ndef write_command(serial, comm, verbose=False, dt=None):\n \"\"\" Encodes a command and sends it over the serial port \"\"\"\n if verbose and comm != '':\n if dt is None:\n print('{} \\t\\t... | [
4,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def change():
name = 'Brill'
print(name)
print(locals())
print(globals())
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def change():
name = 'Brill'
print(n... | flexible | {
"blob_id": "6c7162a9bd81d618abda204c24031c5a5acc61b4",
"index": 7967,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef change():\n name = 'Brill'\n print(name)\n print(locals())\n print(globals())\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\ndef change():\n name = 'Brill'\n ... | [
0,
1,
2,
3,
4
] |
# Generated by Django 3.0.4 on 2020-03-24 16:58
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('students', '0002_auto_20200324_1635'),
]
operations = [
migrations.AddField(
model_name='student',
name='parent_mobi... | normal | {
"blob_id": "a372289d15b55f43887a37bb78a9fc308ddd0371",
"index": 5582,
"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 = [('students', ... | [
0,
1,
2,
3,
4
] |
"""MPI-supported kernels for computing diffusion flux in 2D."""
from sopht.numeric.eulerian_grid_ops.stencil_ops_2d import (
gen_diffusion_flux_pyst_kernel_2d,
gen_set_fixed_val_pyst_kernel_2d,
)
from sopht_mpi.utils.mpi_utils import check_valid_ghost_size_and_kernel_support
from mpi4py import MPI
def gen_dif... | normal | {
"blob_id": "ba8cb18544e4ded8b229bfb9cc4b28599119414f",
"index": 854,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef gen_diffusion_flux_pyst_mpi_kernel_2d(real_t, mpi_construct,\n ghost_exchange_communicator):\n diffusion_flux_pyst_kernel = gen_diffusion_flux_pyst_kernel_2d(real_t=\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def seed_everything(seed):
torch.manual_seed(seed)
torch.cuda.manual_seed(seed)
torch.cuda.manual_seed_all(seed)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = False
np.random.seed(seed)
random.seed(seed)
<|reserved_special_token_0... | flexible | {
"blob_id": "d3b6a105b14d9c3485a71058391a03c2f4aa5c10",
"index": 8628,
"step-1": "<mask token>\n\n\ndef seed_everything(seed):\n torch.manual_seed(seed)\n torch.cuda.manual_seed(seed)\n torch.cuda.manual_seed_all(seed)\n torch.backends.cudnn.deterministic = True\n torch.backends.cudnn.benchmark = ... | [
3,
4,
6,
7,
8
] |
#-------------------------------------------------------------------------------
# Name: module1
# Purpose:
#
# Author: legolas
#
# Created: 05.03.2015
# Copyright: (c) legolas 2015
# Licence: <your licence>
#-------------------------------------------------------------------------------
print "T... | normal | {
"blob_id": "08abb94424598cb54a6b16db68759b216682d866",
"index": 6254,
"step-1": "#-------------------------------------------------------------------------------\n# Name: module1\n# Purpose:\n#\n# Author: legolas\n#\n# Created: 05.03.2015\n# Copyright: (c) legolas 2015\n# Licence: <your li... | [
0
] |
#!/usr/bin/env python3
import sys
import re
from collections import namedtuple
def isnum(name):
return name.startswith('-') or name.isdigit()
class WireValues:
def __init__(self):
self.wires = {}
def __getitem__(self, name):
return int(name) if isnum(name) else self.wires[name]
def _... | normal | {
"blob_id": "a5eb1f559972519dbe0f3702e03af77e61fbfb4e",
"index": 7985,
"step-1": "<mask token>\n\n\nclass WireValues:\n\n def __init__(self):\n self.wires = {}\n\n def __getitem__(self, name):\n return int(name) if isnum(name) else self.wires[name]\n\n def __setitem__(self, name, value):\n... | [
7,
14,
16,
18,
20
] |
<|reserved_special_token_0|>
def evaluate(sess, data, embds, model, logdir):
checkpoint_dir = '{}checkpoints'.format(logdir)
saver = tf.train.Saver()
sess.run(tf.global_variables_initializer())
sess.run(model.embedding_init, feed_dict={model.embedding_placeholder:
embds})
saver.restore(ses... | flexible | {
"blob_id": "3aff6bdfd7c2ffd57af7bb5d0079a8a428e02331",
"index": 1284,
"step-1": "<mask token>\n\n\ndef evaluate(sess, data, embds, model, logdir):\n checkpoint_dir = '{}checkpoints'.format(logdir)\n saver = tf.train.Saver()\n sess.run(tf.global_variables_initializer())\n sess.run(model.embedding_ini... | [
5,
6,
8,
9,
10
] |
<|reserved_special_token_0|>
def mesh_add_vertex_to_face_edge(mesh, key, fkey, v):
"""Add an existing vertex of the mesh to an existing face.
Parameters
----------
mesh : compas.datastructures.Mesh
The mesh data structure.
key : hashable
The identifier of the vertex.
fkey : ha... | flexible | {
"blob_id": "d9b6efce92e30267a9f992c4fea698fe14e0c3e4",
"index": 1398,
"step-1": "<mask token>\n\n\ndef mesh_add_vertex_to_face_edge(mesh, key, fkey, v):\n \"\"\"Add an existing vertex of the mesh to an existing face.\n\n Parameters\n ----------\n mesh : compas.datastructures.Mesh\n The mesh d... | [
1,
2,
3,
4,
5
] |
# Generated by Django 2.0 on 2018-03-06 16:21
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
('digressions', '0004_auto_20180303_1158'),
]
operations = [
migrations.RemoveField(
model_name='ex... | normal | {
"blob_id": "38c21fb959d8b98b616006ea48bd720cc6f9995c",
"index": 1462,
"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 = [('digressions... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def tnrange(*args, **kwargs):
...
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def tqdm_notebook(*args, **kwargs):
...
def tnrange(*args, **kwargs):
...
<|reserved_special_token_1|>
from ._moni... | flexible | {
"blob_id": "25b7af2a8036f35a0bca665867d1729b7c9c113c",
"index": 5846,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef tnrange(*args, **kwargs):\n ...\n",
"step-3": "<mask token>\n\n\ndef tqdm_notebook(*args, **kwargs):\n ...\n\n\ndef tnrange(*args, **kwargs):\n ...\n",
"step-4": "fro... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def f(x):
return np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def f(x):
return np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)
<|reserved_special_token_0|>
plt.plot(xx, y1, '... | flexible | {
"blob_id": "a610ccf4fe154ee12de9212a10958fda2000b425",
"index": 7122,
"step-1": "<mask token>\n\n\ndef f(x):\n return np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef f(x):\n return np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)\n\n\n<mask t... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def dropper():
for ex in affected['word']:
if ex not in model.vocab:
idx_to_drop.append(affected.loc[affected.word == ex].index[0])
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('loading & cleaning the data...')
<... | flexible | {
"blob_id": "f5f26819be4b98fab3d46e57e1a5431e54342aed",
"index": 414,
"step-1": "<mask token>\n\n\ndef dropper():\n for ex in affected['word']:\n if ex not in model.vocab:\n idx_to_drop.append(affected.loc[affected.word == ex].index[0])\n\n\n<mask token>\n",
"step-2": "<mask token>\nprint(... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def inception(image, reuse):
preprocessed = tf.multiply(tf.subtract(tf.expand_dims(image, 0), 0.5), 2.0)
arg_scope = nets.inception.inception_v3_arg_scope(weight_decay=0.0)
with slim.arg_scope(arg_scope):
logits, end_point = nets.inception.inception_v3(preprocessed, 10... | flexible | {
"blob_id": "31d87b11f6a1f6304a2fef6dd1cd1c0ca292dfe8",
"index": 3491,
"step-1": "<mask token>\n\n\ndef inception(image, reuse):\n preprocessed = tf.multiply(tf.subtract(tf.expand_dims(image, 0), 0.5), 2.0)\n arg_scope = nets.inception.inception_v3_arg_scope(weight_decay=0.0)\n with slim.arg_scope(arg_s... | [
5,
6,
7,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
img_path = (pathlib.Path('..') / 'images' / 'tiger.jpg').resolve()
with Image.open(str(img_path)) as img:
print('IMAGE: {}'.format(str(img_path)))
print('Image is in {} format'.fo... | flexible | {
"blob_id": "05edbf3662936465eee8eee0824d1a0cca0df0e5",
"index": 4855,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n img_path = (pathlib.Path('..') / 'images' / 'tiger.jpg').resolve()\n with Image.open(str(img_path)) as img:\n print('IMAGE: {}'.format(str(img_pa... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class CustomUsuarioViewSet(AccessViewSetMixin, mixins.CreateModelMixin,
mixins.RetrieveModelMixin, mixins.ListModelMixin, GenericViewSet):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved... | flexible | {
"blob_id": "43b5936ca9368dcae8d41b44fd9dc927fe18c9bc",
"index": 8794,
"step-1": "<mask token>\n\n\nclass CustomUsuarioViewSet(AccessViewSetMixin, mixins.CreateModelMixin,\n mixins.RetrieveModelMixin, mixins.ListModelMixin, GenericViewSet):\n <mask token>\n <mask token>\n <mask token>\n <mask toke... | [
13,
14,
19,
21,
23
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
setup(name='Antennass', version=VERSION, description=
'A class project that plots far field antenna array patterns',
long_description=README, long_description_content_type='text/markdown',
url='https://github.com/MdeVi... | flexible | {
"blob_id": "f563bb5bb32d3653d8a4115c75eda80b676ae3c6",
"index": 5759,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nsetup(name='Antennass', version=VERSION, description=\n 'A class project that plots far field antenna array patterns',\n long_description=README, long_description_content_type='text... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(dic.get('country', 'Russia'))
<|reserved_special_token_0|>
print(dic)
<|reserved_special_token_1|>
dic = {'city': 'Moscow', 'temperature': 20}
print(dic.get('country', 'Russia'))
dic['date'] = '27.05.2019'
print(dic)
<|... | flexible | {
"blob_id": "f145274c8caa1e725d12003874eb54a580a6e35e",
"index": 784,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(dic.get('country', 'Russia'))\n<mask token>\nprint(dic)\n",
"step-3": "dic = {'city': 'Moscow', 'temperature': 20}\nprint(dic.get('country', 'Russia'))\ndic['date'] = '27.05.2019'\... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.axis([0, 6, 0, 20])
plt.plot(x, y, 'ro')
plt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))
plt.show()
<|reserved_special_token_1|>
<|reserved_special_token_0|>
x = [1, 2, 2.5, 3, 4]
y = [1, 4, 7, 9, 15]
p... | flexible | {
"blob_id": "c69c8ba218935e5bb065b3b925cc7c5f1aa2957b",
"index": 5806,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.axis([0, 6, 0, 20])\nplt.plot(x, y, 'ro')\nplt.plot(np.unique(x), np.poly1d(np.polyfit(x, y, 1))(np.unique(x)))\nplt.show()\n",
"step-3": "<mask token>\nx = [1, 2, 2.5, 3, 4]\ny = [... | [
0,
1,
2,
3,
4
] |
import sys
from PIL import Image
from pr_common import *
file_name = sys.argv[1]
saturation_color = sys.argv[2]
saturation_modifier = int(sys.argv[3])
img = getImage(file_name)
pixels = pixelValues(img)
for i in range(img.height):
for j in range(img.width):
pixel_val = pixels[i][j]
color_idx = No... | normal | {
"blob_id": "96ef95d8997eeab3d85a1bb6e4f8c86c9bfbb0a2",
"index": 4732,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(img.height):\n for j in range(img.width):\n pixel_val = pixels[i][j]\n color_idx = None\n if saturation_color == 'R':\n color_idx = 0\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class SophiesAI(Player):
<|reserved_special_token_0|>
def ship_locations(self) ->Sequence[Tuple[int, int, int, bool]]:
return [(2, 0, 0, True)]
<|reserved_special_token_0|>
def bomb_feedback(self, x: int, y: int, result: ShotResult):
pass
def bombed_... | flexible | {
"blob_id": "127bf47de554dd397d18c6a70616a2a4d93cae80",
"index": 3659,
"step-1": "<mask token>\n\n\nclass SophiesAI(Player):\n <mask token>\n\n def ship_locations(self) ->Sequence[Tuple[int, int, int, bool]]:\n return [(2, 0, 0, True)]\n <mask token>\n\n def bomb_feedback(self, x: int, y: int,... | [
4,
5,
6,
7,
8
] |
import sys
sys.path.append("..\\Pole_IA_Systemes_Experts")
from tkinter import *
from Knowledge_base.Facts import Fact
from Knowledge_base.Rules import Rule
from Backward.Explanation_tree import *
def ask_about_fact(fact: Fact):
"""
Asks the user about whether a fact is true or false threw an interface provi... | normal | {
"blob_id": "4dae34b7c90f52314aac5e457addb3700ffcbd28",
"index": 9156,
"step-1": "<mask token>\n\n\ndef ask_about_fact(fact: Fact):\n \"\"\"\n Asks the user about whether a fact is true or false threw an interface provided by tkinter\n Args:\n fact (Fact): the fact we want to know about\n\n Re... | [
1,
2,
3,
4,
5
] |
a = 1
b = 2
print(a + b)
print("hello")
list = [1, 2, 3, 4, 5]
for i in list:
if i % 2 != 0:
print(i)
print("branch") | normal | {
"blob_id": "03b325094bd3e77f467e17ce54deb95bf2b5c727",
"index": 1724,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(a + b)\nprint('hello')\n<mask token>\nfor i in list:\n if i % 2 != 0:\n print(i)\nprint('branch')\n",
"step-3": "a = 1\nb = 2\nprint(a + b)\nprint('hello')\nlist = [1, 2... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def clicked(num):
current = ent.get()
ent.delete(0, END)
ent.insert(0, str(current) + str(num))
def click_clear():
ent.delete(0, END)
def add():
global ch
ch = '+'
clicked('+')
def subtract():
global ch
ch = '-'
clicked('-')
def multiply():
... | flexible | {
"blob_id": "bdd9ebfa9a2f14d57efd527ca88032bfb0160a5e",
"index": 7504,
"step-1": "<mask token>\n\n\ndef clicked(num):\n current = ent.get()\n ent.delete(0, END)\n ent.insert(0, str(current) + str(num))\n\n\ndef click_clear():\n ent.delete(0, END)\n\n\ndef add():\n global ch\n ch = '+'\n clic... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
class Divide(APIView):
renderer_classes = JSONPRenderer,
@staticmethod
def get(request):
try:
first_number = int(request.GET.get('a'))
second_number = int(request.GET.get('b'))
return Response({'result': first_number / second_number... | flexible | {
"blob_id": "4c483636316dfa660f10b1aba900813bc3e95ebe",
"index": 9463,
"step-1": "<mask token>\n\n\nclass Divide(APIView):\n renderer_classes = JSONPRenderer,\n\n @staticmethod\n def get(request):\n try:\n first_number = int(request.GET.get('a'))\n second_number = int(reques... | [
3,
6,
9,
10,
11
] |
<|reserved_special_token_0|>
class UserProfileAdmin(admin.ModelAdmin):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
class AuditTrailUserAdmin(admin.ModelAdmin):
list_display = 'id', 'date', 'user', 'level', 'message'
list_filter = 'level', 'date', 'user__username'
readonly_fields = [i... | flexible | {
"blob_id": "477d1629c14609db22ddd9fc57cb644508f4f490",
"index": 8905,
"step-1": "<mask token>\n\n\nclass UserProfileAdmin(admin.ModelAdmin):\n <mask token>\n\n\n<mask token>\n\n\nclass AuditTrailUserAdmin(admin.ModelAdmin):\n list_display = 'id', 'date', 'user', 'level', 'message'\n list_filter = 'leve... | [
3,
4,
5,
6,
7
] |
<|reserved_special_token_0|>
class RegistrationFormCaseInsensitive(RegistrationForm):
<|reserved_special_token_0|>
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self.fields[User.USERNAME_FIELD].validators.append(validators.
CaseInsensitiveUnique(User, User... | flexible | {
"blob_id": "3b959481f7c818ec35b8af174b1982954b4c72eb",
"index": 1208,
"step-1": "<mask token>\n\n\nclass RegistrationFormCaseInsensitive(RegistrationForm):\n <mask token>\n\n def __init__(self, *args, **kwargs):\n super().__init__(*args, **kwargs)\n self.fields[User.USERNAME_FIELD].validator... | [
8,
12,
14,
15,
16
] |
<|reserved_special_token_0|>
def tag(name, *content, cls=None, **attrs):
""" 生成一个或多个HTML标签 """
if cls is not None:
attrs['class'] = cls
if attrs:
attrs_str = ''.join(' %s="%s"' % (attr, value) for attr, value in
attrs.items())
else:
attrs_str = ''
if content:
... | flexible | {
"blob_id": "a9b895e4d0830320276359944ca6fdc475fd144e",
"index": 7923,
"step-1": "<mask token>\n\n\ndef tag(name, *content, cls=None, **attrs):\n \"\"\" 生成一个或多个HTML标签 \"\"\"\n if cls is not None:\n attrs['class'] = cls\n if attrs:\n attrs_str = ''.join(' %s=\"%s\"' % (attr, value) for attr... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import phpserialize
import urllib2
from cache import cache
from config import config
def block(request, limit=None):
try:
links = cache.get_cache("sape", expire=3600).get(key="links", createfunc=load_links)
except:
links = cache.get_cache("sape", expi... | normal | {
"blob_id": "6d5acaa4a60b646432feb59f4d8eb9c9d0dceb0f",
"index": 1151,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef block(request, limit=None):\n try:\n links = cache.get_cache('sape', expire=3600).get(key='links',\n createfunc=load_links)\n except:\n links = cach... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
plt.ion()
parser.add_argument('--fish', help="flag for using fisherman's algorithm")
parser.add_argument('--heat', help='flag for using heatmap')
parser.add_argument('--object', help='flag for dumping the clusters')
<|reserved_spe... | flexible | {
"blob_id": "805bc144a4945b46b398853e79ded17370ada380",
"index": 3940,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nplt.ion()\nparser.add_argument('--fish', help=\"flag for using fisherman's algorithm\")\nparser.add_argument('--heat', help='flag for using heatmap')\nparser.add_argument('--object', help... | [
0,
1,
2,
3,
4
] |
from functools import wraps
class aws_retry:
"""retries the call (required for some cases where data is not consistent yet in AWS"""
def __init__(self, fields):
self.fields = fields # field to inject
def __call__(self, function):
pass
#code ... | normal | {
"blob_id": "493b29433f0c3646e7f80fca2f656fc4a5256003",
"index": 8884,
"step-1": "<mask token>\n\n\nclass aws_retry:\n <mask token>\n\n def __init__(self, fields):\n self.fields = fields\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass aws_retry:\n <mask token>\n\n def __init__(self,... | [
2,
3,
4,
5,
6
] |
# 約分して、互いに素な(1,3) (3,1)のようなペアを作りカウントする
# 正のグループと負のグループを別々に管理
# 正のグループの相手が負のグループに存在した場合、
# どちらかのグループから好きなだけ選ぶか、どちらも選ばないかしかない
# 誰ともペアにならなかったグループの個数を全て足してP個だとして、2^P通りを掛ける
# (0,0)については、その中から1つ選ぶか、選ばないかしかない
import sys
readline = sys.stdin.readline
N = int(readline())
import math
zeropair = 0
zeroa = 0
zerob = 0
from coll... | normal | {
"blob_id": "098488fd10bcf81c4efa198a44d2ff87e4f8c130",
"index": 3225,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor i in range(N):\n a, b = map(int, readline().split())\n if a == 0 and b == 0:\n zeropair += 1\n continue\n if a == 0:\n zeroa += 1\n continue\n ... | [
0,
1,
2,
3,
4
] |
from src.MultiValueDictApp import MultiValueDictApp
def main():
app = MultiValueDictApp()
print("Welcome to Multivalue Dictionary App")
print("COMMANDS and format:")
print("KEYS")
print("MEMBERS key")
print("ADD key value")
print("REMOVE key value")
print("REMOVEALL key")
print("CLE... | normal | {
"blob_id": "21e83369c4100c41885e9ee8a8d7310556bfe51d",
"index": 7271,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef main():\n app = MultiValueDictApp()\n print('Welcome to Multivalue Dictionary App')\n print('COMMANDS and format:')\n print('KEYS')\n print('MEMBERS key')\n prin... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class Results_flat(models.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_t... | flexible | {
"blob_id": "802eb0502c5eddcabd41b2d438bf53a5d6fb2c82",
"index": 8368,
"step-1": "<mask token>\n\n\nclass Results_flat(models.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
# coding: utf-8
# MIT Licensed
# http://opensource.org/licenses/MIT
led_dir = "/sys/class/gpio/gpio40/"
led_pin = led_dir + "value"
led_mode = led_dir + "direction"
with open(led_mode, "wb") as f:
f.write("out")
with open(led_pin, "wb") as f:
f.write(__import__("sys").argv[1])
"""
Contrib... | normal | {
"blob_id": "1a9cad6e49e5ed2bb7781f9fec930d48ec048b3b",
"index": 5061,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(led_mode, 'wb') as f:\n f.write('out')\nwith open(led_pin, 'wb') as f:\n f.write(__import__('sys').argv[1])\n<mask token>\n",
"step-3": "led_dir = '/sys/class/gpio/gpio4... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class Recipe:
<|reserved_special_token_0|>
def __init__(self, labor_amount: float, required_goods: BagOfGoods,
planet_variation: Distribution, person_variation: Distribution,
labor_variation: Distribution, output_good: GoodKind):
self.labor_amount = labor_... | flexible | {
"blob_id": "286801b69546046853d123c5708f24eaaa2e8cec",
"index": 6044,
"step-1": "<mask token>\n\n\nclass Recipe:\n <mask token>\n\n def __init__(self, labor_amount: float, required_goods: BagOfGoods,\n planet_variation: Distribution, person_variation: Distribution,\n labor_variation: Distrib... | [
6,
9,
17,
23,
26
] |
<|reserved_special_token_0|>
def make_Folders(names):
for n in names:
if not os.path.exists(n):
os.makedirs(n)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def make_Folders(names):
for n in names:
if not os.path.exists(n):
... | flexible | {
"blob_id": "426396c981fe56230e39b81e156e7c6877e39055",
"index": 2213,
"step-1": "<mask token>\n\n\ndef make_Folders(names):\n for n in names:\n if not os.path.exists(n):\n os.makedirs(n)\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef make_Folders(names):\n for n in names:\n ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class User(db.Model):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
... | flexible | {
"blob_id": "0b2bc19aea9393562f79df026bc17513e25c6604",
"index": 8535,
"step-1": "<mask token>\n\n\nclass User(db.Model):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask ... | [
14,
15,
16,
17,
19
] |
from otree.api import Currency as c, currency_range
from . import models
from ._builtin import Page, WaitPage
from .models import Constants
class Introduction(Page):
timeout_seconds = 60
class Welcome(Page):
timeout_seconds = 60
class Priming(Page):
form_model = models.Player
form_fields = ['text'... | normal | {
"blob_id": "8fecfdf4b3772e5304f0b146317f94cdbd7fbd53",
"index": 5791,
"step-1": "<mask token>\n\n\nclass Eye11(Page):\n form_model = models.Player\n form_fields = ['option_11']\n timeout_seconds = 10\n\n\nclass Eye12(Page):\n form_model = models.Player\n form_fields = ['option_12']\n timeout_s... | [
76,
81,
85,
100,
105
] |
# ----------------------------------------------------------------------
# Numenta Platform for Intelligent Computing (NuPIC)
# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement
# with Numenta, Inc., for a separate license for this software code, the
# following terms and conditions apply:
#
# This progra... | normal | {
"blob_id": "f0f4573808253ca4bff808104afa9f350d305a9c",
"index": 3501,
"step-1": "# ----------------------------------------------------------------------\n# Numenta Platform for Intelligent Computing (NuPIC)\n# Copyright (C) 2013, Numenta, Inc. Unless you have an agreement\n# with Numenta, Inc., for a separate... | [
0
] |
import os
templateFile = 'crab_template.py'
samples=[\
#"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM",
#"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v2/MINIAODSIM", #Identical? Same event count #miniAO... | normal | {
"blob_id": "184b850e85b523f22a44cfde698efd96b94d819d",
"index": 2095,
"step-1": "import os\ntemplateFile = 'crab_template.py'\nsamples=[\\\n#\"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-tauola/Spring14miniaod-PU20bx25_POSTLS170_V5-v1/MINIAODSIM\", \n#\"/TTJets_MSDecaysCKM_central_Tune4C_13TeV-madgraph-t... | [
0
] |
class CacheDecorator:
<|reserved_special_token_0|>
def cachedFunc(self, *args):
if args not in self.cache:
print('Ergebnis berechnet')
self.cache[args] = self.func(*args)
else:
print('Ergebnis geladen')
return self.cache[args]
def __call__(self, ... | flexible | {
"blob_id": "b7f6207fe6c013a964258255445004c3f4e0adbb",
"index": 7217,
"step-1": "class CacheDecorator:\n <mask token>\n\n def cachedFunc(self, *args):\n if args not in self.cache:\n print('Ergebnis berechnet')\n self.cache[args] = self.func(*args)\n else:\n p... | [
3,
4,
5,
6,
7
] |
# coding: utf-8
import os, sys
import numpy as np
from math import exp, sqrt, pi
def factorial(n):
value = 1
for i in range(n,1,-1):
value *= i
return value
def double_factorial(n):
k = 1
for i in range(n, 1, -2):
k *= i
#print("n:", n, "double factorial:", k)
return k
... | normal | {
"blob_id": "005650e2747c61b730960a29891b6ba6c8bd381b",
"index": 1334,
"step-1": "<mask token>\n\n\ndef double_factorial(n):\n k = 1\n for i in range(n, 1, -2):\n k *= i\n return k\n\n\n<mask token>\n\n\ndef gaussian_integral(alpha, m):\n if int(m / 2) * 2 == m:\n n = int(m / 2)\n ... | [
2,
3,
4,
5,
6
] |
"""
You are given two arrays (without duplicates) nums1 and nums2 where nums1’s elements are subset of nums2. Find all the next greater numbers for nums1's elements in the corresponding places of nums2.
The Next Greater Number of a number x in nums1 is the first greater number to its right in nums2. If it does not exi... | normal | {
"blob_id": "3abeac4fb80244d2da14e14a6048c09b0c0c1393",
"index": 6047,
"step-1": "<mask token>\n\n\nclass Solution(object):\n <mask token>\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution(object):\n\n def nextGreaterElement(self, findNums, nums):\n \"\"\"\n :type findNums: ... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
try:
from local_settings import *
except ImportError:
pass
if SENTRY_URL:
import sentry_sdk
sentry_sdk.init(SENTRY_URL)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
WOO_HOST = os.environ.get('WOO_HO... | flexible | {
"blob_id": "386fa51b9b285d36c75d6446f9348f6713e0dbaa",
"index": 2794,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n from local_settings import *\nexcept ImportError:\n pass\nif SENTRY_URL:\n import sentry_sdk\n sentry_sdk.init(SENTRY_URL)\n",
"step-3": "<mask token>\nWOO_HOST = os.... | [
0,
1,
2,
3,
4
] |
from tkinter import *
root = Tk()
ent = Entry(root)
ent.pack()
def click():
ent_text = ent.get()
lab = Label(root, text=ent_text)
lab.pack()
btn = Button(root, text="Click Me!", command=click)
btn.pack()
root.mainloop()
| normal | {
"blob_id": "49f1b4c9c6d15b8322b83396c22e1027d241da33",
"index": 2311,
"step-1": "<mask token>\n\n\ndef click():\n ent_text = ent.get()\n lab = Label(root, text=ent_text)\n lab.pack()\n\n\n<mask token>\n",
"step-2": "<mask token>\nent.pack()\n\n\ndef click():\n ent_text = ent.get()\n lab = Label... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def get_all_countries():
response = requests.get('{}/countries'.format(ROOT_URL))
return response.json()['countries']
def get_country_probability(countryIds):
body = {'countryIds': countryIds}
response = requests.get('{}/countries/probability'.format(ROOT_URL),
d... | flexible | {
"blob_id": "6aa7114db66a76cfa9659f5537b1056f40f47bd2",
"index": 3975,
"step-1": "<mask token>\n\n\ndef get_all_countries():\n response = requests.get('{}/countries'.format(ROOT_URL))\n return response.json()['countries']\n\n\ndef get_country_probability(countryIds):\n body = {'countryIds': countryIds}\... | [
11,
12,
15,
17,
18
] |
import glob
import logging
import os
import sqlite3
from aiogram import Bot, Dispatcher, executor, types
TOKEN = '1772334389:AAE5wv8gssOFOgxQjQwKk7rUSKQHr6NTjus'
logging.basicConfig(level=logging.INFO)
bot = Bot(token=TOKEN)
dp = Dispatcher(bot)
path1 = 'C:\\Users\\const\\PycharmProjects\\t'
conn = sqlite3.connect('... | normal | {
"blob_id": "4193fa992d06890afb660c072842cf1b85a43774",
"index": 3207,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nlogging.basicConfig(level=logging.INFO)\n<mask token>\n\n\n@dp.message_handler(commands=['start', 'help'])\nasync def send_welcome(message: types.Message):\n await message.reply(\n ... | [
0,
1,
2,
3,
4
] |
from django.shortcuts import render, redirect, get_object_or_404
from django.http import HttpResponse
from django.contrib import messages
# Create your views here.
from User.models import User, check_if_auth_user
from .models import Chat
# def recv_chat(request, id = None):
# check = check_if_auth_user(request)
# if... | normal | {
"blob_id": "9dfb3f58127b30467651ac4209277cd947643c65",
"index": 7411,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef begin_chat(request, id=None):\n check = check_if_auth_user(request)\n if not check:\n messages.error(request, 'Perform login first to start chatting')\n return... | [
0,
1,
2,
3
] |
import matplotlib
matplotlib.use('Agg')
import matplotlib.gridspec as gridspec
import matplotlib.pyplot as plt
import numpy as np
from mpl_toolkits.axes_grid1 import make_axes_locatable
def plot_overscan(overscan, img, TITLE, OUT_DIR):
""" plot overscan in 9x2 plots with 16 channels """
fig = plt.figure(figs... | normal | {
"blob_id": "736861f18936c7a87ecf3deb134f589b9d7eed92",
"index": 3934,
"step-1": "\nimport matplotlib\nmatplotlib.use('Agg')\nimport matplotlib.gridspec as gridspec\nimport matplotlib.pyplot as plt\nimport numpy as np\nfrom mpl_toolkits.axes_grid1 import make_axes_locatable\n\n\ndef plot_overscan(overscan, img, ... | [
0
] |
# -*- coding: utf-8 -*-
import os
import sys
import base64
import cdutil
import json
import os
from array import array
from uuid import uuid4
import cdms2
import numpy as np
import matplotlib as mpl
mpl.rcParams['mathtext.default'] = 'regular'
mpl.use('qt4agg')
import matplotlib.pyplot as plt
from mpl_toolkits.ba... | normal | {
"blob_id": "ff9376ab4d6a88849167fb6e180fd9c4f9ab4dad",
"index": 8283,
"step-1": " # -*- coding: utf-8 -*-\nimport os\nimport sys\n\nimport base64\nimport cdutil\nimport json\nimport os\nfrom array import array\nfrom uuid import uuid4\n\nimport cdms2\nimport numpy as np\nimport matplotlib as mpl\nmpl.rcParams... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
counter1 = Hmm(3)
input_counts = open('gene_NoClass.counts', 'r')
dev_file = open('gene.dev', 'r+')
output_file2 = open('gene_dev.NoClass.out.p2', 'w')
print('dev_file read')
prin... | flexible | {
"blob_id": "6dda23cc5d0083e72520b0664b6550ccb48e4b4f",
"index": 7288,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n counter1 = Hmm(3)\n input_counts = open('gene_NoClass.counts', 'r')\n dev_file = open('gene.dev', 'r+')\n output_file2 = open('gene_dev.NoClass.ou... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class TeacherGUI:
<|reserved_special_token_0|>
@classmethod
def setup(cls, ui_mainwindow):
cls.__ui_mainwindow = ui_mainwindow
@classmethod
def display_all_active_school_classes(cls, school_classes):
cls.__ui_mainwindow.tableWidget_14.clear()
... | flexible | {
"blob_id": "98f234ca0cbec419466de0504fd8d5c68fd07627",
"index": 9609,
"step-1": "<mask token>\n\n\nclass TeacherGUI:\n <mask token>\n\n @classmethod\n def setup(cls, ui_mainwindow):\n cls.__ui_mainwindow = ui_mainwindow\n\n @classmethod\n def display_all_active_school_classes(cls, school_c... | [
100,
103,
113,
122,
132
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('signup/', views.signup, name='signup'), path('home',
views.home, name='home'), path('collab/', views.collab, name='collab')]
<|reserved_special_token_1|>
from django.urls import path
from django.conf.ur... | flexible | {
"blob_id": "351963bee76ecaa9fa5c8d659f6d7c6ca9b22531",
"index": 2182,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('signup/', views.signup, name='signup'), path('home',\n views.home, name='home'), path('collab/', views.collab, name='collab')]\n",
"step-3": "from django.urls im... | [
0,
1,
2,
3
] |
def main():
s1 = 'mabaabm'
s2 = 'moktko!'
s3 = ex7(s1, s2)
print(s3)
def ex7(in1, in2):
out1 = in1[0] + in1[int(len(in1) / 2)] + in1[int(len(in1) - 1)] + in2[0
] + in2[int(len(in2) / 2)] + in2[int(len(in2) - 1)]
return out1
if __name__ == '__main__':
main()
| normal | {
"blob_id": "f45cae397aa3b7bdba6e3f36e20b926487cb160d",
"index": 9238,
"step-1": "<mask token>\n",
"step-2": "def main():\n s1 = 'mabaabm'\n s2 = 'moktko!'\n s3 = ex7(s1, s2)\n print(s3)\n\n\n<mask token>\n",
"step-3": "def main():\n s1 = 'mabaabm'\n s2 = 'moktko!'\n s3 = ex7(s1, s2)\n ... | [
0,
1,
2,
3
] |
import requests
import re
import time
import os
import argparse
import json
url = "https://contactform7.com/captcha/"
headers = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_14_5) AppleWebKit/605.1.15 (KHTML, like Gecko) Version/12.1.1 Safari/605.1.15',
'Content-Type': "multipart/form-data; boun... | normal | {
"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
] |
<|reserved_special_token_0|>
def resultados(request, total):
latest_question_list = Pregunta.objects.order_by('fecha')[:total]
output = ', '.join([q.descripcion for q in latest_question_list])
return HttpResponse(output)
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_tok... | flexible | {
"blob_id": "07dc058ecef323ffd41299245e4fcafdc9e41506",
"index": 2131,
"step-1": "<mask token>\n\n\ndef resultados(request, total):\n latest_question_list = Pregunta.objects.order_by('fecha')[:total]\n output = ', '.join([q.descripcion for q in latest_question_list])\n return HttpResponse(output)\n\n\n<... | [
1,
2,
3,
4,
5
] |
#coding: utf-8
import numpy as np
import cv2
leftgray = cv2.imread('../image/1.jpg')
rightgray = cv2.imread('../image/2.jpg')
hessian=500
surf=cv2.xfeatures2d.SURF_create(hessian) #将Hessian Threshold设置为400,阈值越大能检测的特征就越少
kp1,des1=surf.detectAndCompute(leftgray,None) #查找关键点和描述符
kp2,des2=surf.detectAndCompute(rightgr... | normal | {
"blob_id": "60953878c377382f1c7f25ce284c9fa12b8eb25f",
"index": 4667,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.namedWindow('mathches', 1)\ncv2.imshow('mathches', a)\ncv2.waitKey()\n<mask token>\nfor m, n in matches:\n if m.distance < 0.45 * n.distance:\n good.append(m)\nprint(len(goo... | [
0,
1,
2,
3,
4
] |
''' Load a variety of relevant physical parameters.
All quantities are in atomic units, such that
m_e = 1
e = 1
hbar = 1
1/4\pi\epsilon = 1
'''
import numpy as np
hbar = 1.0
m_e = 1.0
h22m = hbar**2 / (2*m_e)
pi = np.pi
eV = 1/27.21138505
eV_Ha = eV
nm = 18.89726124565
kB_eV = 8.6173324e-5
kB = kB_e... | normal | {
"blob_id": "f9f835b24aa8fc77109db9e2d89a3f43bcb4b181",
"index": 7079,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhbar = 1.0\nm_e = 1.0\nh22m = hbar ** 2 / (2 * m_e)\npi = np.pi\neV = 1 / 27.21138505\neV_Ha = eV\nnm = 18.89726124565\nkB_eV = 8.6173324e-05\nkB = kB_eV * eV_Ha\n",
"step-3": "<mask to... | [
0,
1,
2,
3
] |
# Jeremy Jao
# University of Pittsburgh: DBMI
# 6/18/2013
#
# This is the thing that returns the dictionary of the key. we can edit more code to return different values in the keys (gene) in each dictionary inside the dictionary.
# my sys.argv isn't working in my situation due to my IDE (nor do I not know how it w... | normal | {
"blob_id": "7016a7dda80c0cfae0e15cf239f6ae64eb9004b7",
"index": 9733,
"step-1": "# Jeremy Jao\r\n# University of Pittsburgh: DBMI\r\n# 6/18/2013\r\n#\r\n# This is the thing that returns the dictionary of the key. we can edit more code to return different values in the keys (gene) in each dictionary inside the d... | [
0
] |
# Copyright 2016 Tesora, Inc.
# All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License"); you may
# not use this file except in compliance with the License. You may obtain
# a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by ... | normal | {
"blob_id": "5e29c6d1034f6612b0081037f8dc679b49f1dbef",
"index": 2855,
"step-1": "<mask token>\n",
"step-2": "charset = {'big5': ['big5_chinese_ci', 'big5_bin'], 'dec8': [\n 'dec8_swedish_ci', 'dec8_bin'], 'cp850': ['cp850_general_ci',\n 'cp850_bin'], 'hp8': ['hp8_english_ci', 'hp8_bin'], 'koi8r': [\n ... | [
0,
1,
2
] |
#coding=utf-8
'''
Created on 2013-3-28
@author: jemmy
'''
import telnetlib
import getpass
import sys
import os
import time
import xlrd
from pyExcelerator import *
import
#define
Host = "192.168.0.1"
Port = "70001"
#Host = raw_iput("IP",)
username = "admin"
password = "admin"
filename = str(time.strftime('%Y%m%d%H%M%S... | normal | {
"blob_id": "153c02585e5d536616ec4b69757328803ac2fb71",
"index": 3394,
"step-1": "#coding=utf-8\n'''\nCreated on 2013-3-28\n\n@author: jemmy\n'''\nimport telnetlib\nimport getpass\nimport sys\nimport os\nimport time\nimport xlrd\nfrom pyExcelerator import *\nimport\n#define\nHost = \"192.168.0.1\"\nPort = \"7000... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Map(BaseCommand):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Map(BaseCommand):
def run(self):
from lib.models import Mapping
from lib.models i... | flexible | {
"blob_id": "07783921da2fb4ae9452324f833b08b3f92ba294",
"index": 546,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Map(BaseCommand):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Map(BaseCommand):\n\n def run(self):\n from lib.models import Mapping\n from lib.mod... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/python
# -*- coding: utf-8 -*-
import json
import urllib2
#this is executed by a cron job on the pi inside the pooltable
secret ='secret'
baseurl='https://pooltable.mysite.com/'
url = baseurl + 'gettrans.php?secret=' + secret
req = urllib2.Request(url)
f = urllib2.urlopen(req)
response = f.read()... | normal | {
"blob_id": "9baf55eb2fb70e9fa0d92df22d307962b8d6c6d4",
"index": 5883,
"step-1": "#!/usr/bin/python\r\n# -*- coding: utf-8 -*-\r\n\r\nimport json\r\nimport urllib2\r\n#this is executed by a cron job on the pi inside the pooltable\r\nsecret ='secret'\r\nbaseurl='https://pooltable.mysite.com/'\r\nurl = baseurl + '... | [
0
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
bind = '0.0.0.0:8000'
workers = os.environ['GET_KEYS_ACCOUNTS_WORKERS']
<|reserved_special_token_1|>
import os
bind = '0.0.0.0:8000'
workers = os.environ['GET_KEYS_ACCOUNTS_WORKERS']
| flexible | {
"blob_id": "d84a7e16471c604283c81412653e037ecdb19102",
"index": 3530,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nbind = '0.0.0.0:8000'\nworkers = os.environ['GET_KEYS_ACCOUNTS_WORKERS']\n",
"step-3": "import os\nbind = '0.0.0.0:8000'\nworkers = os.environ['GET_KEYS_ACCOUNTS_WORKERS']\n",
"step-4... | [
0,
1,
2
] |
<|reserved_special_token_0|>
@api.route('/get/<key_name>', methods=['GET'])
def get(key_name):
li = db_handle(key_name)
if li[1] is None:
abort(404)
else:
result = matchtyper(li)
return make_response(jsonify(result))
@api.errorhandler(404)
def not_found(error):
return make_re... | flexible | {
"blob_id": "44e9fd355bfab3f007c5428e8a5f0930c4011646",
"index": 3853,
"step-1": "<mask token>\n\n\n@api.route('/get/<key_name>', methods=['GET'])\ndef get(key_name):\n li = db_handle(key_name)\n if li[1] is None:\n abort(404)\n else:\n result = matchtyper(li)\n return make_response... | [
2,
3,
4,
5
] |
# -*- coding: utf-8 -*-
from __future__ import unicode_literals
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('courses', '0015_auto_20151216_1136'),
]
operations = [
migrations.AlterField(
model_name='duration',
... | normal | {
"blob_id": "0cba18ca7126dda548a09f34dc26b83d6471bf68",
"index": 1652,
"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 = [('courses', '... | [
0,
1,
2,
3,
4
] |
n=int(0)
import random
def doubleEven(n):
if n % 2 == 0:
n = n*2
return (n)
else:
return "-1"
print(doubleEven(n = int(input("put in a number"))))
g=int(0)
def grade(g):
if g < 50:
return "F"
if g < 66:
return "C"
if g > 92:
return "A+"
else:
... | normal | {
"blob_id": "5251724656e1d971900fff3d8fa0210c6cfc27bb",
"index": 5505,
"step-1": "n=int(0)\nimport random\ndef doubleEven(n):\n if n % 2 == 0:\n n = n*2\n return (n)\n else:\n return \"-1\"\n\n\nprint(doubleEven(n = int(input(\"put in a number\"))))\n\ng=int(0)\n\ndef grade(g):\n if... | [
0
] |
<|reserved_special_token_0|>
class Game(object):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def render_field(self):
"""Метод отрисовки поля"""
print(tabulate.tabulate(self.rendered_field, tablefmt='grid'))
def check_free_place(self, i, j):
"""Метод проверки кле... | flexible | {
"blob_id": "23ba9e498dd153be408e973253d5f2a858d4771b",
"index": 6922,
"step-1": "<mask token>\n\n\nclass Game(object):\n <mask token>\n <mask token>\n\n def render_field(self):\n \"\"\"Метод отрисовки поля\"\"\"\n print(tabulate.tabulate(self.rendered_field, tablefmt='grid'))\n\n def c... | [
5,
6,
9,
11,
12
] |
#!/usr/bin/env python
import cgitb
import cgi
import pymysql
form = cgi.FieldStorage()
c.execute("SELECT * FROM example")
recs = c.fetchall()
records1 = """
<body>
<table>
<tbody>
<tr>
<th>Full Name</th>
<th>Average Score</th>
</tr>"""
records_dyn = [
f"<tr><td>{name}</td><td>{avg... | normal | {
"blob_id": "b5fee01582a28085983c56b9c266ef7fd5c3c927",
"index": 5132,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nc.execute('SELECT * FROM example')\n<mask token>\nprint('Content-Type:text/html; charset=utf-8')\nprint()\nfor i in records1.split('\\n'):\n print(i)\nfor i in records_dyn:\n print(... | [
0,
1,
2,
3,
4
] |
# Copyright 2018-present Kensho Technologies, LLC.
from .utils import create_vertex_statement, get_random_date, get_uuid
EVENT_NAMES_LIST = (
"Birthday",
"Bar Mitzvah",
"Coronation",
"Re-awakening",
)
def _create_event_statement(event_name):
"""Return a SQL statement to create a Event vertex."""... | normal | {
"blob_id": "a521befba58aa85c2fcfe6006db4b161123585f1",
"index": 5341,
"step-1": "<mask token>\n\n\ndef _create_event_statement(event_name):\n \"\"\"Return a SQL statement to create a Event vertex.\"\"\"\n field_name_to_value = {'name': event_name, 'event_date':\n get_random_date(), 'uuid': get_uuid... | [
1,
2,
3,
4,
5
] |
IMAGE_SIZE=(640, 480)
| normal | {
"blob_id": "af80cb4d4ce5c071efc39e85f89bb412cff6bf6e",
"index": 4489,
"step-1": "<mask token>\n",
"step-2": "IMAGE_SIZE = 640, 480\n",
"step-3": "IMAGE_SIZE=(640, 480)\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
1,
2
]
} | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if boxChecked == 'true':
heading = 'Recurring Donation'
customerRequest = {'given_name': firstName, 'family_name': lastName,
'email_address': email}
try:
customerResponse = customers_api_instance.create... | flexible | {
"blob_id": "bb7910af5334641fd2db7146112afaff7a2e42b9",
"index": 565,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif boxChecked == 'true':\n heading = 'Recurring Donation'\n customerRequest = {'given_name': firstName, 'family_name': lastName,\n 'email_address': email}\n try:\n c... | [
0,
1,
2,
3,
4
] |
# from https://github.com/tensorflow/models/tree/master/research/object_detection/dataset_tools
# and https://gist.github.com/saghiralfasly/ee642af0616461145a9a82d7317fb1d6
import tensorflow as tf
from object_detection.utils import dataset_util
import os
import io
import hashlib
import xml.etree.ElementTree as ET
imp... | normal | {
"blob_id": "8142585827590f6d951f0fcc375e8511aa75e9c8",
"index": 7320,
"step-1": "<mask token>\n\n\ndef main(_):\n writer_train = tf.python_io.TFRecordWriter('./data/train.record')\n writer_test = tf.python_io.TFRecordWriter('./data/test.record')\n filename_list = tf.train.match_filenames_once('./data/a... | [
1,
2,
3,
4,
5
] |
n=int(input("Digite um número"))
m=n-1
o=n+1
print("Seu número é {} seu antecessor é {} e seu sucessor é {}".format(n,m,o)) | normal | {
"blob_id": "47d72379b894826dad335f098649702ade195f78",
"index": 7337,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Seu número é {} seu antecessor é {} e seu sucessor é {}'.format(n, m, o)\n )\n",
"step-3": "n = int(input('Digite um número'))\nm = n - 1\no = n + 1\nprint('Seu número é {} se... | [
0,
1,
2,
3
] |
def unique(lisst):
setlisst = set(lisst)
return len(setlisst)
print(unique({4, 5, 1, 1, 3}))
| normal | {
"blob_id": "42d26ef51bb4dafc8a0201a828652e166a3905e4",
"index": 7339,
"step-1": "<mask token>\n",
"step-2": "def unique(lisst):\n setlisst = set(lisst)\n return len(setlisst)\n\n\n<mask token>\n",
"step-3": "def unique(lisst):\n setlisst = set(lisst)\n return len(setlisst)\n\n\nprint(unique({4, ... | [
0,
1,
2
] |
#!/usr/bin/env python
"""This script draws a boxplot of each atom contribution to the cavity."""
import sys
if sys.version < "2.7":
print >> sys.stderr, "ERROR: This script requires Python 2.7.x. "\
"Please install it and try again."
exit(1)
try:
import matplotlib.pyplot as pyp... | normal | {
"blob_id": "9fdcaf65f070b7081afd327442dd20e3284c71eb",
"index": 7905,
"step-1": "<mask token>\n\n\ndef parse_args():\n import argparse\n import os.path\n\n def isfile(path):\n Error = argparse.ArgumentTypeError\n if not os.path.exists(path):\n raise Error(\"No such file: '{0}'\... | [
6,
7,
8,
11,
12
] |
class Node:
def __init__(self, data):
self.data = data
self.prev = None
self.next = None
class LinkedList:
def __init__(self):
self.head = None
def insertAtHead(self, newNode, curNode):
newNode.next = curNode
if curNode is not None: curNode.prev = newNode
... | normal | {
"blob_id": "a3cbdecbbfc49e8ac045f4aabbea6b9f54ed3d5f",
"index": 4904,
"step-1": "<mask token>\n\n\nclass LinkedList:\n\n def __init__(self):\n self.head = None\n\n def insertAtHead(self, newNode, curNode):\n newNode.next = curNode\n if curNode is not None:\n curNode.prev = ... | [
5,
7,
8,
9,
11
] |
#!/usr/bin/env python
#
# ConVirt - Copyright (c) 2008 Convirture Corp.
# ======
#
# ConVirt is a Virtualization management tool with a graphical user
# interface that allows for performing the standard set of VM operations
# (start, stop, pause, kill, shutdown, reboot, snapshot, etc...). It
# also attempts to s... | normal | {
"blob_id": "3078a0c7e2c711da88846ca3401c7924b1790dbc",
"index": 1251,
"step-1": "#!/usr/bin/env python\n#\n# ConVirt - Copyright (c) 2008 Convirture Corp.\n# ======\n#\n# ConVirt is a Virtualization management tool with a graphical user\n# interface that allows for performing the standard set of VM opera... | [
0
] |
def multiplica():
one = int(input('1º: '))
two = int(input('2º: '))
print('a multiplicação é: ', one*two)
def soma():
one = int(input('1º: '))
two = int(input('2º: '))
print('a soma é: ', one+two)
def subtra():
one = int(input('1º: '))
two = int(input('2º: '))
... | normal | {
"blob_id": "414fa4021b21cea0dc49380aebfe67f0204f0574",
"index": 5994,
"step-1": "def multiplica():\n one = int(input('1º: '))\n two = int(input('2º: '))\n print('a multiplicação é: ', one * two)\n\n\ndef soma():\n one = int(input('1º: '))\n two = int(input('2º: '))\n print('a soma é: ', one + ... | [
2,
3,
4,
5,
6
] |
from pyspark import SparkContext, RDD
from pyspark.sql import SparkSession, DataFrame
from pyspark.streaming import StreamingContext
from pyspark.streaming.kafka import KafkaUtils
import string
from kafka import KafkaProducer
import time
import pyspark
sc = SparkContext(master='local[4]')
ssc = StreamingContext(sc, b... | normal | {
"blob_id": "12fdeae0ae1618139b20176846e7df5b82f7aa01",
"index": 8274,
"step-1": "<mask token>\n\n\ndef send_rdd(rdd):\n out_list = rdd.collect()\n for word in out_list:\n producer.send('had2020011-out', value=str(word))\n\n\n<mask token>\n\n\ndef aggregator(values, old):\n return (old or 0) + su... | [
2,
3,
4,
5,
6
] |
# Generated by Django 3.0.4 on 2020-04-04 11:07
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('product', '0003_cost'),
... | normal | {
"blob_id": "a4f2ca3155f2bb4c17be5bb56dd889abb5d20293",
"index": 3791,
"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 = [migrations.sw... | [
0,
1,
2,
3,
4
] |
"""David's first approach when I exposed the problem.
Reasonable to add in the comparison?
"""
import numpy as np
from sklearn.linear_model import RidgeCV
from sklearn.model_selection import ShuffleSplit
def correlation(x, y):
a = (x - x.mean(0)) / x.std(0)
b = (y - y.mean(0)) / y.std(0)
return a.T @ b / ... | normal | {
"blob_id": "dfd2b515e08f285345c750bf00f6a55f43d60039",
"index": 8379,
"step-1": "<mask token>\n\n\ndef partial_correlation_loop(solver, x, y, ensemble=None):\n e_hat = np.zeros(y.shape[1])\n for i in range(y.shape[1]):\n y_i = y[:, i].reshape(-1, 1)\n y_not_i = np.delete(y, i, axis=1)\n ... | [
4,
5,
6,
7,
9
] |
import Net
import mnist_parser
import numpy as np
#To use this model it is required to download the MNIST database
#The donwloaded base is then needet parse to numpy using mnist_parser.parse_to_npy method
#The files genetared using mnist_parser.parse_to_npy are then loaded using np.load
in_values = np.load("MNIST/mnist... | normal | {
"blob_id": "49005500b299ca276f663fe8431bb955e5585bbd",
"index": 335,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n net = Net.FeedForwardNet(input_count=784, layers=[100, 10],\n activation_function=Net.FeedForwardNet.leaky_relu)\n try:\n epoch_num = int(input('Epoch_num... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AutomationserverConfig(AppConfig):
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class AutomationserverConfig(AppConfig):
name = 'automationserver'
<|reserved_special... | flexible | {
"blob_id": "3153218fe1d67fdc1c1957ffcfdb380688c159c1",
"index": 6483,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass AutomationserverConfig(AppConfig):\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass AutomationserverConfig(AppConfig):\n name = 'automationserver'\n",
"step-4": "... | [
0,
1,
2,
3
] |
from utils import create_data_lists
if __name__ == '__main__':
create_data_lists(ICDAR_path=
'../ICDAR_Dataset/0325updated.task1train(626p)', output_folder=
'../ICDAR_Dataset/0325updated.task1train(626p)')
| normal | {
"blob_id": "6334a8a052d72b0f13395b301bd5a766acf4399b",
"index": 3437,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n create_data_lists(ICDAR_path=\n '../ICDAR_Dataset/0325updated.task1train(626p)', output_folder=\n '../ICDAR_Dataset/0325updated.task1train(62... | [
0,
1,
2
] |
<|reserved_special_token_0|>
def sieve(n):
sieve = [1] * (n + 1)
sieve[1] = 0
sieve[0] = 0
for i in range(2, int(math.sqrt(n) + 1)):
if sieve[i] == 1:
for j in range(i * i, n + 1, i):
sieve[j] = 0
return sieve
def odd_prime(a):
while a != 0:
y = a ... | flexible | {
"blob_id": "60617ff6eda880e5467b3b79d3df13a7147f5990",
"index": 3329,
"step-1": "<mask token>\n\n\ndef sieve(n):\n sieve = [1] * (n + 1)\n sieve[1] = 0\n sieve[0] = 0\n for i in range(2, int(math.sqrt(n) + 1)):\n if sieve[i] == 1:\n for j in range(i * i, n + 1, i):\n ... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class external(terrascript.Provider):
pass
<|reserved_special_token_1|>
import terrascript
class external(terrascript.Provider):
pass
<|reserved_special_token_1|>
# terrascript/external/__init__.py
import terras... | flexible | {
"blob_id": "04e57739e6fb98cd237fbe09caecd17c728c1797",
"index": 5548,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass external(terrascript.Provider):\n pass\n",
"step-3": "import terrascript\n\n\nclass external(terrascript.Provider):\n pass\n",
"step-4": "# terrascript/external/__init... | [
0,
1,
2,
3
] |
import os
from django.core.asgi import get_asgi_application
os.environ.setdefault('DJANGO_SETTINGS_MODULE',
'ecommerce.settings.development')
application = get_asgi_application()
| normal | {
"blob_id": "1cb320cf57823511b0398adce097b770b2131eb6",
"index": 9307,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'ecommerce.settings.development')\n<mask token>\n",
"step-3": "<mask token>\nos.environ.setdefault('DJANGO_SETTINGS_MODULE',\n 'e... | [
0,
1,
2,
3
] |
from Receiver import Receiver
import time
import Image
class Sender:
ACK = []
size = None
windowSize = None
tableOfFrames = []
ChosenSumAlgorithm = None
def __init__(self, receiver):
self.receiver = receiver
pass
def send_frame(self, frame):
self.receiver.receiver_... | normal | {
"blob_id": "ecbcd023b8fec5763c6ff7f4cd0999426fae4a50",
"index": 9093,
"step-1": "<mask token>\n\n\nclass Sender:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def send_frame(self, frame):\n self.receiver.receiver_frame(frame)\n p... | [
5,
7,
8,
10,
11
] |
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