code stringlengths 13 6.09M | order_type stringclasses 2
values | original_example dict | step_ids listlengths 1 5 |
|---|---|---|---|
<|reserved_special_token_0|>
def test_oembed_founded(oembed_providers):
oembed_url_extractor = oembed.OEmbedURLExtractor(oembed_providers)
url = 'https://www.instagram.com/p/BNHh2YJDdcY/'
oembed_url = oembed_url_extractor.get_oembed_url(url)
assert isinstance(oembed_url, str)
def test_oembed_discove... | flexible | {
"blob_id": "7b2ad0b4eca7b31b314e32ad57d51be82f0eaf61",
"index": 6979,
"step-1": "<mask token>\n\n\ndef test_oembed_founded(oembed_providers):\n oembed_url_extractor = oembed.OEmbedURLExtractor(oembed_providers)\n url = 'https://www.instagram.com/p/BNHh2YJDdcY/'\n oembed_url = oembed_url_extractor.get_o... | [
2,
3,
4,
5
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def run(args):
min = -0.0
max = 0.5
Q = 10
if os.path.isfile(args.incat):
cbc.coaddBatchCutFull(args.root, args.incat, filter=args.filter,
idField=args.idField, prefix=args.prefix, zCutoutSize... | flexible | {
"blob_id": "c0503536672aa824eaf0d19b9d4b5431ef910432",
"index": 1028,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef run(args):\n min = -0.0\n max = 0.5\n Q = 10\n if os.path.isfile(args.incat):\n cbc.coaddBatchCutFull(args.root, args.incat, filter=args.filter,\n id... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class ContentsBlockIterator(BaseBlockIterator):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ContentsBlockIterator(BaseBlockIterator):
def __init__(self, *args, **kwargs):
super().__init... | flexible | {
"blob_id": "b888745b3ce815f7c9eb18f5e76bacfadfbff3f5",
"index": 3153,
"step-1": "<mask token>\n\n\nclass ContentsBlockIterator(BaseBlockIterator):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass ContentsBlockIterator(BaseBlockIterator):\n\n def __init__(self, *args, **kwargs):\n ... | [
1,
2,
3,
4
] |
import matplotlib.pyplot as plt
import cv2
# 0
img = cv2.imread('test.jpg', cv2.IMREAD_GRAYSCALE)
# IMREAD_COLOR = 1
# IMREAD_UNCHANGED = -1
cv2.imshow('image', img)
cv2.waitKey(0)
cv2.destroyAllWindows()
# cv2.imwrite('watchgray,png', img)
plt.imshow(img, cmap='gray', interpolation='bicu... | normal | {
"blob_id": "34ccaaf5eb47afd556588cd94cddbddaee1f0b53",
"index": 2851,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ncv2.imshow('image', img)\ncv2.waitKey(0)\ncv2.destroyAllWindows()\nplt.imshow(img, cmap='gray', interpolation='bicubic')\nplt.show()\n",
"step-3": "<mask token>\nimg = cv2.imread('test.... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@adapter(IProcessStarting)
def start_successlogging(unused):
"""start successlogging if configured."""
from App.config import getConfiguration
config = getConfiguration().product_config.get('successlogging')
if config is None:
return
global _log_good, _log_bad
... | flexible | {
"blob_id": "2edbf18c90da1ff40fd9abaf25a35dbdaf733bc1",
"index": 2786,
"step-1": "<mask token>\n\n\n@adapter(IProcessStarting)\ndef start_successlogging(unused):\n \"\"\"start successlogging if configured.\"\"\"\n from App.config import getConfiguration\n config = getConfiguration().product_config.get('... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
class IsOwnerOrStaffOrReadOnly(BasePermission):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class IsOwnerOrStaffOrReadOnly(BasePermission):
def has_object_permission(self, request, view, obj):
... | flexible | {
"blob_id": "4488612164435ab062ca66000f0d7dc3ccd89da2",
"index": 8150,
"step-1": "<mask token>\n\n\nclass IsOwnerOrStaffOrReadOnly(BasePermission):\n <mask token>\n <mask token>\n",
"step-2": "<mask token>\n\n\nclass IsOwnerOrStaffOrReadOnly(BasePermission):\n\n def has_object_permission(self, request... | [
1,
2,
3,
4
] |
<|reserved_special_token_0|>
def check_compound_set(description_mol, validate_dict):
y_m_d = description_mol.GetProp('generation_date').split('-')
submitter_dict = {'submitter__name': description_mol.GetProp(
'submitter_name'), 'submitter__email': description_mol.GetProp(
'submitter_email'), '... | flexible | {
"blob_id": "0082f75332321dba498f06d4c4a99c9248829b59",
"index": 654,
"step-1": "<mask token>\n\n\ndef check_compound_set(description_mol, validate_dict):\n y_m_d = description_mol.GetProp('generation_date').split('-')\n submitter_dict = {'submitter__name': description_mol.GetProp(\n 'submitter_name... | [
9,
10,
14,
16,
18
] |
####
# This is the script for storing the schema of your TerminusDB
# database for your project.
# Use 'terminusdb commit' to commit changes to the database and
# use 'terminusdb sync' to change this file according to
# the exsisting database schema
####
from typing import List
from terminusdb_client.woqlschema impor... | normal | {
"blob_id": "f702cdef3782ddc96244f3cf8e2026581d60baa9",
"index": 1537,
"step-1": "<mask token>\n\n\nclass State(DocumentTemplate):\n _key = ValueHashKey()\n country: 'Country'\n name: str\n",
"step-2": "<mask token>\n\n\nclass Address(DocumentTemplate):\n <mask token>\n city: 'City'\n coordin... | [
2,
13,
14,
15,
16
] |
<|reserved_special_token_0|>
class AllabolaSpider(scrapy.Spider):
<|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_specia... | flexible | {
"blob_id": "d60a2100127db859162890204655d313cdc2a4a5",
"index": 4614,
"step-1": "<mask token>\n\n\nclass AllabolaSpider(scrapy.Spider):\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 f.write('fn,ln,zip,ct,st,co... | [
2,
4,
5,
6,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class ScenesMiddleware(BaseMiddleware):
<|reserved_special_token_0|>
async def on_post_process_message(self, message: types.Message, results:
tuple, data: dict):
if data:
return
user_... | flexible | {
"blob_id": "11db76cba3dd76cad0d660a0e189d3e4c465071b",
"index": 8836,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ScenesMiddleware(BaseMiddleware):\n <mask token>\n\n async def on_post_process_message(self, message: types.Message, results:\n tuple, data: dict):\n if data... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
@app.route('/', methods=['GET', 'POST'])
def home():
if request.method == 'POST':
entry_content = request.form.get('content')
output = client.specific_resource_analysis(body={'document': {
'text': entry_content}}, params={'language': language,
'... | flexible | {
"blob_id": "d0f2d47a786b85367f96897e7cd8c2ef8c577e2b",
"index": 2961,
"step-1": "<mask token>\n\n\n@app.route('/', methods=['GET', 'POST'])\ndef home():\n if request.method == 'POST':\n entry_content = request.form.get('content')\n output = client.specific_resource_analysis(body={'document': {\... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def solution2(a, b):
answer = [(a[i] * b[i]) for i in range(len(a))]
return sum(answer)
<|reserved_special_token_0|>
def solution5(a, b):
answer = sum([(i * j) for i, j in zip(a, b)])
return answer
<|reserved_special_token_1|>
def solution(a, b):
answer = 0
... | flexible | {
"blob_id": "5b8322761975ebec76d1dccd0290c0fb1da404e5",
"index": 5999,
"step-1": "<mask token>\n\n\ndef solution2(a, b):\n answer = [(a[i] * b[i]) for i in range(len(a))]\n return sum(answer)\n\n\n<mask token>\n\n\ndef solution5(a, b):\n answer = sum([(i * j) for i, j in zip(a, b)])\n return answer\n... | [
2,
3,
4,
5,
6
] |
# import draw as p
# ако няма __init__.py
# from draw.point import Point
from draw import Rectangle
from draw import Point
from draw import ShapeUtils
if __name__ == '__main__':
pn1 = Point(9,8)
pn2 = Point(6,4)
print(f'dist: {pn1} and {pn1} = {ShapeUtils.distance(pn1,pn2)}')
rc1 = Rectangle(4... | normal | {
"blob_id": "b984dc052201748a88fa51d25c3bd3c22404fa96",
"index": 6571,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n pn1 = Point(9, 8)\n pn2 = Point(6, 4)\n print(f'dist: {pn1} and {pn1} = {ShapeUtils.distance(pn1, pn2)}')\n rc1 = Rectangle(40, 20, 120, 300)\n ... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def first_append_to_last(arr):
return arr + [arr[0]]
<|reserved_special_token_0|>
def RMS(arr):
n = len(arr)
sq_sum = sum(a ** 2 for a in arr)
return (sq_sum / n) ** 0.5
<|reserved_special_token_0|>
def L1(P1, P2):
x1, y1 = P1
x2, y2 = P2
return abs(x2 ... | flexible | {
"blob_id": "8bbc929e2ff2321b97195031fa675fbdab269fcb",
"index": 3288,
"step-1": "<mask token>\n\n\ndef first_append_to_last(arr):\n return arr + [arr[0]]\n\n\n<mask token>\n\n\ndef RMS(arr):\n n = len(arr)\n sq_sum = sum(a ** 2 for a in arr)\n return (sq_sum / n) ** 0.5\n\n\n<mask token>\n\n\ndef L1... | [
4,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
np.put(x, ind=idx, v=1)
print(x)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
x = np.zeros(10)
idx = [1, 4, 5, 9]
np.put(x, ind=idx, v=1)
print(x)
<|reserved_special_token_1|>
import numpy as np
x = np.zeros(10)... | flexible | {
"blob_id": "9e2485554a5a8de07dd3df39cc255f2a1ea2f164",
"index": 4769,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nnp.put(x, ind=idx, v=1)\nprint(x)\n",
"step-3": "<mask token>\nx = np.zeros(10)\nidx = [1, 4, 5, 9]\nnp.put(x, ind=idx, v=1)\nprint(x)\n",
"step-4": "import numpy as np\nx = np.zeros(... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
def RED(t):
GPIO.output(21, 1)
time.sleep(1)
GPIO.output(21, 0)
<|reserved_special_token_0|>
def dataTransfer(conn):
while True:
data = conn.recv(1024)
data = data.decode('utf-8')
dataMessage = data.split(' ', 1)
command = dataMessage[0]... | flexible | {
"blob_id": "78efe97d838774cb831ef205186db29f392e1953",
"index": 1584,
"step-1": "<mask token>\n\n\ndef RED(t):\n GPIO.output(21, 1)\n time.sleep(1)\n GPIO.output(21, 0)\n\n\n<mask token>\n\n\ndef dataTransfer(conn):\n while True:\n data = conn.recv(1024)\n data = data.decode('utf-8')\n... | [
2,
4,
7,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open(join(here, 'VERSION')) as VERSION_FILE:
__versionstr__ = VERSION_FILE.read().strip()
with open(join(here, 'requirements.txt')) as REQUIREMENTS:
INSTALL_REQUIRES = REQUIREMENTS.read().split('\n')
with io.open(join... | flexible | {
"blob_id": "8d5978bc579115eb3065dce1bae08f1790f2d83c",
"index": 2832,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(join(here, 'VERSION')) as VERSION_FILE:\n __versionstr__ = VERSION_FILE.read().strip()\nwith open(join(here, 'requirements.txt')) as REQUIREMENTS:\n INSTALL_REQUIRES = REQ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class AdaBoostClassifier:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def predict(self, X, threshold=0):
"""Predict the ca... | flexible | {
"blob_id": "905d8be76ef245a2b8fcfb3f806f8922d351ecf0",
"index": 8877,
"step-1": "<mask token>\n\n\nclass AdaBoostClassifier:\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n def predict(self, X, threshold=0):\n \"\"\"Predict the catagories... | [
3,
7,
8,
9,
12
] |
import gc
import unittest
import numpy as np
from pydrake.autodiffutils import AutoDiffXd
from pydrake.common import RandomDistribution, RandomGenerator
from pydrake.common.test_utilities import numpy_compare
from pydrake.common.test_utilities.deprecation import catch_drake_warnings
from pydrake.common.value import Va... | normal | {
"blob_id": "f17ae8a44f8b032feac7c18fe39663054fea40c0",
"index": 5282,
"step-1": "<mask token>\n\n\nclass TestGeneral(unittest.TestCase):\n\n def _check_instantiations(self, template, supports_symbolic=True):\n default_cls = template[None]\n self.assertTrue(template[float] is default_cls)\n ... | [
25,
26,
28,
30,
35
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
hp.mollview(fu, title='Full map +50 GLAT', sub=311)
hp.mollview(se, title='Above threshold (4.0) +50 GLAT', sub=312)
hp.mollview(ma, title='Diff +50 GLAT', sub=313)
plt.savefig('figs/diff4a.pdf', bbox_inches='tight', pad_inches=0.... | flexible | {
"blob_id": "d86fd2e6ef5dab4444772192471538842112b3fd",
"index": 2675,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nhp.mollview(fu, title='Full map +50 GLAT', sub=311)\nhp.mollview(se, title='Above threshold (4.0) +50 GLAT', sub=312)\nhp.mollview(ma, title='Diff +50 GLAT', sub=313)\nplt.savefig('figs/d... | [
0,
1,
2,
3,
4
] |
import sys
import utils
#import random
def findNearestPoint(points,no_used , src):
# If no nearest point found, return max.
dest = src
minDist = sys.float_info.max
for i in range(len(points)):
if no_used[i] and i!=src:
dist = utils.length(points[src], poi... | normal | {
"blob_id": "943db90aa7721ddad3d7f5103c4d398fbf4e143b",
"index": 2768,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef findNearestPoint(points, no_used, src):\n dest = src\n minDist = sys.float_info.max\n for i in range(len(points)):\n if no_used[i] and i != src:\n dist ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('report.csv') as csvfile:
data = csv.reader(csvfile, delimiter=',')
for row in data:
p = tf.add_paragraph()
p.text = row[0]
p.level = 1
p = tf.add_paragraph()
p.text = row[... | flexible | {
"blob_id": "e1f003b6a687e5654a1ee6c595e789ced02cd6c3",
"index": 7086,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('report.csv') as csvfile:\n data = csv.reader(csvfile, delimiter=',')\n for row in data:\n p = tf.add_paragraph()\n p.text = row[0]\n p.level = 1\n ... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class conv2D:
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def forward(self, input_feature_maps):
output = np.zeros(self.output_shape)
input_feature_maps = self.apply_zero_padding(input_feature_maps)
for i in range(0, self.kernel_shape[0])... | flexible | {
"blob_id": "ff99b5fd168d7987e488d7f6d0455619e988f15a",
"index": 3574,
"step-1": "<mask token>\n\n\nclass conv2D:\n <mask token>\n <mask token>\n\n def forward(self, input_feature_maps):\n output = np.zeros(self.output_shape)\n input_feature_maps = self.apply_zero_padding(input_feature_map... | [
24,
25,
28,
33,
39
] |
import random
import time
import unittest
from old import dict_groupby
class TestDictGroupBy(unittest.TestCase):
def setUp(self):
random.seed(0)
self.sut = dict_groupby
def generate_transaction(self):
return {
'transaction_type': random.choice(['a', 'b', 'c']),
... | normal | {
"blob_id": "f8e6f6e1be6c4ea306b7770c918b97808a0765b2",
"index": 6580,
"step-1": "<mask token>\n\n\nclass TestDictGroupBy(unittest.TestCase):\n\n def setUp(self):\n random.seed(0)\n self.sut = dict_groupby\n <mask token>\n\n def generate_facility(self):\n num_transactions = random.r... | [
4,
7,
8,
9,
10
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for sequence_file in sequences:
f_in = open(current_dir + '/sample_genomes/' + sequence_file, 'r')
f_out.write(f_in.read())
f_in.close()
data = []
fa_file = current_dir + '/sample_genomes/' + sequence_file
... | flexible | {
"blob_id": "9696e5799d46adb5b92c0900e2064b927addfd93",
"index": 2506,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor sequence_file in sequences:\n f_in = open(current_dir + '/sample_genomes/' + sequence_file, 'r')\n f_out.write(f_in.read())\n f_in.close()\n data = []\n fa_file = curre... | [
0,
1,
2,
3,
4
] |
<|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_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.... | flexible | {
"blob_id": "ead843f1edcfe798613effb049e3ca79dcd03b71",
"index": 7919,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
# this is the example code from the t0p-level README..d
from spatialmath import SE3
import roboticstoolbox as rtb
import swift
robot = rtb.models.DH.Panda()
print(robot)
T = robot.fkine(robot.qz)
print(T)
# IK
T = SE3(0.7, 0.2, 0.1) * SE3.OA([0, 1, 0], [0, 0, -1])
sol = robot.ikine_LMS(T) # solve IK, ignore additio... | normal | {
"blob_id": "cc1a1491ffbcf470705aeea079faac290dbaa25e",
"index": 5965,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(robot)\n<mask token>\nprint(T)\n<mask token>\nprint(sol.q)\nprint(robot.fkine(sol.q))\n<mask token>\nrobot.plot(qtraj.q, movie='panda1.gif')\n<mask token>\nprint(robot)\n<mask token... | [
0,
1,
2,
3,
4
] |
from django.db import models
from django.core.validators import RegexValidator, MaxValueValidator
# from Delivery.models import Delivery
# from Customers.models import Customer, Address, Order, Item
# Create your models here.
class Restaurant(models.Model):
Restaurant_ID = models.AutoField(primary_key=True)
... | normal | {
"blob_id": "7ea1ee7c55cd53f7137c933790c3a22957f0ffea",
"index": 4987,
"step-1": "<mask token>\n\n\nclass Food(models.Model):\n Food_ID = models.AutoField(primary_key=True)\n Food_Name = models.CharField(max_length=250)\n Food_Pic = models.ImageField(upload_to='Restaurants/Pictures/Food')\n Food_Cate... | [
2,
4,
5,
6,
8
] |
#determines where the robot is located.
def sense(p, Z, colors, sensor_right):
#initialization
q = []
pHit = sensor_right;
pMiss = 1 - sensor_right;
#number of rows
m = len(colors)
#number of columns
n = len(colors[0])
#sum
s = 0
for i in range(m):
temp = []
... | normal | {
"blob_id": "10937ee1e48d23b12b76a2abc44ee8bd0647aef5",
"index": 9248,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef localize(colors, measurements, motions, sensor_right, p_move):\n p = []\n m = len(colors)\n n = len(colors[0])\n size = m * n\n for i in range(m):\n temp = [... | [
0,
1,
2,
3,
4
] |
#!/usr/bin/env python
import mcvine.cli
from numpy import array
from mcvine_workflow.singlextal.resolution import use_res_comps as urc
beam_neutrons_path = '/SNS/users/p63/ORNL_public_research/MCViNE_Covmat_comparison/mcvine_resolution/beams/beam_125_1e9/out/neutrons'
instrument = urc.instrument('ARCS', '3.*meter', '13... | normal | {
"blob_id": "47c5fb03cb427d5c9f7703e1715e026b6f2c7a35",
"index": 4660,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurc.run(beam_neutrons_path, instrument, samplexmlpath, psi, hkl2Q, pixel,\n t_m2p, Q, E, hkl_projection, Nbuffer=100000)\n",
"step-3": "<mask token>\nbeam_neutrons_path = (\n '/SN... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.usefixtures('driver')
class BaseClass:
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
@pytest.mark.usefixtures('driver')
class BaseClass:
"""BaseClass takes in dr... | flexible | {
"blob_id": "1b49cb59ebdb548cfc7567cd5cb4affe30f33aac",
"index": 5576,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.usefixtures('driver')\nclass BaseClass:\n <mask token>\n",
"step-3": "<mask token>\n\n\n@pytest.mark.usefixtures('driver')\nclass BaseClass:\n \"\"\"BaseClass tak... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print('Добрый день,', name)
<|reserved_special_token_1|>
name = input('Введите ваше имя ')
print('Добрый день,', name)
<|reserved_special_token_1|>
name = input("Введите ваше имя ")
print("Добрый день,", name)
| flexible | {
"blob_id": "e44c4b2c3b60d34d4540ec2d3a782c777c52fbc0",
"index": 8662,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint('Добрый день,', name)\n",
"step-3": "name = input('Введите ваше имя ')\nprint('Добрый день,', name)\n",
"step-4": "name = input(\"Введите ваше имя \")\nprint(\"Добрый день,\", n... | [
0,
1,
2,
3
] |
#Some people are standing in a queue. A selection process follows a rule where people standing on even positions are selected. Of the selected people a queue is formed and again out of these only people on even position are selected. This continues until we are left with one person. Find out the position of that person... | normal | {
"blob_id": "358fd8efd5c3823255ab64d5f8b88b343415ed0e",
"index": 2708,
"step-1": "def even(n):\n if n == 0 or n == 1:\n return\n elif n == 2:\n return 2\n else:\n for i in reversed(range(n + 1)):\n if 2 ** i < n:\n return 2 ** i\n\n\n<mask token>\n",
"ste... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def get_amount():
"""Get valid donation amount from user"""
while True:
try:
amount = input('How much did they donate: ')
if str(amount).lower() == 'exit':
return amount
else:
return float(amount)
... | flexible | {
"blob_id": "8a192fc08a65c80b8733a9d07374156c09f36598",
"index": 2823,
"step-1": "<mask token>\n\n\ndef get_amount():\n \"\"\"Get valid donation amount from user\"\"\"\n while True:\n try:\n amount = input('How much did they donate: ')\n if str(amount).lower() == 'exit':\n ... | [
6,
7,
8,
9,
12
] |
#Web Scraping
#Make sure you have bs4, webbrowser and request installed as your third party modules
import bs4, webbrowser, requests
try:
data = requests.get("http://en.wikipedia.org/wiki/Python")
data.raise_for_status()
my_data = bs4.BeautifulSoup(data.text, "lxml")
print("List o... | normal | {
"blob_id": "27e9635adf6109f3ab13b9d8dd5809973b61ca03",
"index": 413,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ntry:\n data = requests.get('http://en.wikipedia.org/wiki/Python')\n data.raise_for_status()\n my_data = bs4.BeautifulSoup(data.text, 'lxml')\n print('List of all the header tag... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
print('CrawlerSlaveYoke')
print('CSY-000000023.py')
<|reserved_special_token_1|>
# Author: Andreas Francois Vermeulen
print("CrawlerSlaveYoke")
print("CSY-000000023.py")
| flexible | {
"blob_id": "322795bce189428823c45a26477555052c7d5022",
"index": 8933,
"step-1": "<mask token>\n",
"step-2": "print('CrawlerSlaveYoke')\nprint('CSY-000000023.py')\n",
"step-3": "# Author: Andreas Francois Vermeulen\nprint(\"CrawlerSlaveYoke\")\nprint(\"CSY-000000023.py\")\n",
"step-4": null,
"step-5": nu... | [
0,
1,
2
] |
def pixels_generator(w, h):
i = 0
while i < (w * h):
yield divmod(i, w)
i = i + 1
| normal | {
"blob_id": "bb481fa038835abc6d61a4985b1e30c7c00bff96",
"index": 158,
"step-1": "<mask token>\n",
"step-2": "def pixels_generator(w, h):\n i = 0\n while i < w * h:\n yield divmod(i, w)\n i = i + 1\n",
"step-3": "def pixels_generator(w, h):\n i = 0\n while i < (w * h):\n yield... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
for category in ('beauty', 'fashion', 'mobile'):
with open('%s/%s_data_info_val_competition.csv' % (data_dir, category), 'r'
) as infile:
next(infile)
for line in infile:
curr_id = line.stri... | flexible | {
"blob_id": "82556291c456b9e43e4e589ea4a77d320430344b",
"index": 7478,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nfor category in ('beauty', 'fashion', 'mobile'):\n with open('%s/%s_data_info_val_competition.csv' % (data_dir, category), 'r'\n ) as infile:\n next(infile)\n for ... | [
0,
1,
2,
3
] |
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
from sklearn import linear_model
from features import calculateTargets
currency = 'EURUSD'
interval = '1440'
df = pd.read_csv(
r'../data/' + currency.upper() + interval + '.csv',
names=['date', 'time', 'open', 'high', 'low', 'close', 'volu... | normal | {
"blob_id": "8c0bae9e49c5ea9fbdee7c5c864afff16cc9f8b8",
"index": 3757,
"step-1": "import matplotlib.pyplot as plt\nimport numpy as np\nimport pandas as pd\nfrom sklearn import linear_model\nfrom features import calculateTargets\n\ncurrency = 'EURUSD'\ninterval = '1440'\n\ndf = pd.read_csv(\n r'../data/' + cur... | [
0
] |
<|reserved_special_token_0|>
def _mako_get_namespace(context, name):
try:
return context.namespaces[__name__, name]
except KeyError:
_mako_generate_namespaces(context)
return context.namespaces[__name__, name]
<|reserved_special_token_0|>
def _mako_inherit(template, context):
_... | flexible | {
"blob_id": "57967f36a45bb3ea62708bbbb5b2f4ddb0f4bb16",
"index": 29,
"step-1": "<mask token>\n\n\ndef _mako_get_namespace(context, name):\n try:\n return context.namespaces[__name__, name]\n except KeyError:\n _mako_generate_namespaces(context)\n return context.namespaces[__name__, nam... | [
3,
5,
6,
7,
8
] |
from random import random
def random_numbers():
print('start generator')
while True:
val = random()
print(f'will yield {val}')
yield val
def run_random_numbers():
print(f'{random_numbers=}')
rnd_gen = random_numbers()
print(f'{rnd_gen=}')
print(f'{next(rnd_gen)=}')
... | normal | {
"blob_id": "e5979aeb7cff0e2a75966924382bae87aebcfcb2",
"index": 3312,
"step-1": "<mask token>\n\n\ndef exercise_gen(ret_val, times):\n \"\"\"Return `ret_value` `times` times.\n If generator will receive some value from outside, update `ret_value`\"\"\"\n\n\ndef exercise1():\n \"\"\"Make it pass\"\"\"\n... | [
2,
4,
6,
9,
10
] |
import os
import random
import pygame
# Class for all the game's obstacles
class Obstacle(pygame.sprite.Sprite):
# Class constructor
def __init__(self, game_params, game_speed):
self.obs_type = random.randrange(0, 3)
# Becomes a pterodactyl obstacle
if (self.obs_type == 0):
... | normal | {
"blob_id": "09dac7bfe98a15b3e79edcb0d0a53c0ab4d771ca",
"index": 7053,
"step-1": "<mask token>\n\n\nclass Obstacle(pygame.sprite.Sprite):\n\n def __init__(self, game_params, game_speed):\n self.obs_type = random.randrange(0, 3)\n if self.obs_type == 0:\n self.create_pterodactyl(game_p... | [
7,
8,
9,
10,
11
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def find_roads(probability_map, *, input_threshold=0.3, max_roads=None,
min_strength=0.17, num_angles=720, roads_min_angle=np.pi / 8,
roads_min_distance=50, debugimage=None, debugprint=None):
"""
Finds full-image... | flexible | {
"blob_id": "f76185095ebb1adbf7ae22ffb500ffc3d6b0a30d",
"index": 6019,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef find_roads(probability_map, *, input_threshold=0.3, max_roads=None,\n min_strength=0.17, num_angles=720, roads_min_angle=np.pi / 8,\n roads_min_distance=50, debugimage=None,... | [
0,
3,
4,
5,
6
] |
"""Test Assert module."""
import unittest
from physalia import asserts
from physalia.fixtures.models import create_random_sample
from physalia.models import Measurement
# pylint: disable=missing-docstring
class TestAssert(unittest.TestCase):
TEST_CSV_STORAGE = "./test_asserts_db.csv"
def setUp(self):
... | normal | {
"blob_id": "eda1c1db5371f5171f0e1929e98d09e10fdcef24",
"index": 1677,
"step-1": "<mask token>\n\n\nclass TestAssert(unittest.TestCase):\n <mask token>\n <mask token>\n\n def test_consumption_below(self):\n sample = create_random_sample(10, 1)\n asserts.consumption_below(sample, 11)\n ... | [
4,
5,
6,
7,
8
] |
#connect4_JayNa.py
#Jay Na
#CS111 Spring 2018
#This file creates a version of the game Connect4, where the user plays against an AI
from graphics import *
import random
class ConnectWindow:
def __init__(self):
self.window = GraphWin("Connect Four", 690, 590)
self.window.setMouseHandler(self.handleClick)
self.... | normal | {
"blob_id": "abbad57e945d2195021948a0e0838c6bfd9c6a1e",
"index": 769,
"step-1": "<mask token>\n\n\nclass ConnectWindow:\n <mask token>\n\n def startScreen(self):\n \"\"\"This function creates the board and intializes the board count for each column\"\"\"\n self.background = Rectangle(Point(0,... | [
2,
8,
10,
11,
16
] |
from ipyleaflet import Map, DrawControl, Marker, Rectangle
from sentinelhub import BBox, CRS
from ipywidgets import widgets as w
class BBoxSelector:
def __init__(self, bbox, zoom=8, resolution=10):
center = (bbox.min_y + bbox.max_y) / 2, (bbox.min_x + bbox.max_x) / 2
self.map = Map(center=center,... | normal | {
"blob_id": "0545aff80e19e47cb9e5b1941e92ff5cb109f9e6",
"index": 1921,
"step-1": "<mask token>\n\n\nclass BBoxSelector:\n\n def __init__(self, bbox, zoom=8, resolution=10):\n center = (bbox.min_y + bbox.max_y) / 2, (bbox.min_x + bbox.max_x) / 2\n self.map = Map(center=center, zoom=zoom, scroll_w... | [
3,
4,
5,
6,
7
] |
<|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": "53519c704ca9aff62140f187d4246208350fa9ba",
"index": 4610,
"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 = [('PDPAPI', '0... | [
0,
1,
2,
3,
4
] |
<|reserved_special_token_0|>
class PinEnvDiscrete(Env):
<|reserved_special_token_0|>
def __init__(self, simulation, x, y, trajectory, scorer=0,
max_displacement=False, predict=False, original=False, sample=False):
self.simulation = simulation
height, width = simulation.cloth.initial_p... | flexible | {
"blob_id": "21974274b1e7800b83eb9582ab21714f04230549",
"index": 4299,
"step-1": "<mask token>\n\n\nclass PinEnvDiscrete(Env):\n <mask token>\n\n def __init__(self, simulation, x, y, trajectory, scorer=0,\n max_displacement=False, predict=False, original=False, sample=False):\n self.simulatio... | [
8,
9,
10,
12,
13
] |
<|reserved_special_token_0|>
class ebiz_supplier_account_create(osv.osv_memory):
<|reserved_special_token_0|>
<|reserved_special_token_0|>
def create_supplier_action(self, cr, uid, ids, context=None):
active_ids = context.get('active_ids', False)
supplier_ids = self.pool['ebiz.supplier.ac... | flexible | {
"blob_id": "309f8016dfebcc3595291b127edb4634f72298ec",
"index": 4387,
"step-1": "<mask token>\n\n\nclass ebiz_supplier_account_create(osv.osv_memory):\n <mask token>\n <mask token>\n\n def create_supplier_action(self, cr, uid, ids, context=None):\n active_ids = context.get('active_ids', False)\n... | [
2,
4,
5,
6,
7
] |
import os
from linkedin_scraper import get_jobs
chrome_driver_path = os.path.join(os.path.abspath(os.getcwd()), 'chromedriver')
df = get_jobs('Data Scientist', 40, False, chrome_driver_path)
df.to_csv('linkedin_jobs.csv', index=False)
| normal | {
"blob_id": "6ae529a5e5658ba409ec3e7284d8b2911c60dd00",
"index": 906,
"step-1": "<mask token>\n",
"step-2": "<mask token>\ndf.to_csv('linkedin_jobs.csv', index=False)\n",
"step-3": "<mask token>\nchrome_driver_path = os.path.join(os.path.abspath(os.getcwd()), 'chromedriver')\ndf = get_jobs('Data Scientist', ... | [
0,
1,
2,
3
] |
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
import random
PATH = "C:\\Program Files (x86)\\chromedriver.exe"
destination = "https://news.ycombinator.com/"
class hackernewsUpvoter():
def __init__(self, username, password, website):
self.driver = webdriver.Chro... | normal | {
"blob_id": "742b655ee6aad2575f67e7329ed7a14c4fb6aa06",
"index": 7242,
"step-1": "<mask token>\n\n\nclass hackernewsUpvoter:\n <mask token>\n\n def sign_in(self, login_page='https://news.ycombinator.com/login'):\n self.driver.get(login_page)\n time.sleep(2)\n account = self.driver.find... | [
4,
5,
7,
8,
10
] |
# This is a sample Python script.
# Press Shift+F10 to execute it or replace it with your code.
# Press Double Shift to search everywhere for classes, files, tool windows, actions, and settings.
from PyQt5 import QtWidgets, uic
import sys
import pymysql
import mysql.connector
class Ui_Login(QtWidgets.QDi... | normal | {
"blob_id": "0ff6e22f8704a0c6c0ffff3c53761b9d3a531b6d",
"index": 683,
"step-1": "<mask token>\n\n\nclass Ui_Login(QtWidgets.QDialog):\n\n def __init__(self):\n super(Ui_Login, self).__init__()\n uic.loadUi('login.ui', self)\n self.icon = self.findChild(QtWidgets.QLabel, 'ilogin')\n ... | [
5,
7,
8,
9,
10
] |
import serial
import mysql.connector
ser = serial.Serial('/dev/serial0', 9600)
while True:
data = ser.readline()
if data[0]==";":
print(data)
data = data.split(";")
if data[1] == "1":
fonction = data[1]
add = data[2]
tmp = data[3]
debit = data[4]
ser.write([123])
#test affichage
print "Sa... | normal | {
"blob_id": "b1a6593e7b528238e7be5ea6da4d1bfee0d78067",
"index": 7824,
"step-1": "import serial\nimport mysql.connector\n\nser = serial.Serial('/dev/serial0', 9600)\n\nwhile True:\n\tdata = ser.readline()\n\tif data[0]==\";\":\n\t\tprint(data)\n\t\tdata = data.split(\";\")\n\t\tif data[1] == \"1\":\n\t\t\tfoncti... | [
0
] |
<|reserved_special_token_0|>
def ms_matrices(E, Q, matrix_terms, dim):
"""Compute the Möller-Stetter matrices in the monomial basis from a
reduced Macaulay matrix
Parameters
----------
E : (m, k) ndarray
Columns of the reduced Macaulay matrix corresponding to the quotient basis
Q : (l... | flexible | {
"blob_id": "14fb6776ac30802edf43c43acbee64263c6bdd7b",
"index": 2777,
"step-1": "<mask token>\n\n\ndef ms_matrices(E, Q, matrix_terms, dim):\n \"\"\"Compute the Möller-Stetter matrices in the monomial basis from a\n reduced Macaulay matrix\n\n Parameters\n ----------\n E : (m, k) ndarray\n ... | [
5,
6,
8,
10,
12
] |
#
# @lc app=leetcode id=267 lang=python3
#
# [267] Palindrome Permutation II
#
# https://leetcode.com/problems/palindrome-permutation-ii/description/
#
# algorithms
# Medium (33.28%)
# Total Accepted: 24.8K
# Total Submissions: 74.4K
# Testcase Example: '"aabb"'
#
# Given a string s, return all the palindromic perm... | normal | {
"blob_id": "4e538251dedfe0b9ffb68de2de7dc50681320f1f",
"index": 8619,
"step-1": "#\n# @lc app=leetcode id=267 lang=python3\n#\n# [267] Palindrome Permutation II\n#\n# https://leetcode.com/problems/palindrome-permutation-ii/description/\n#\n# algorithms\n# Medium (33.28%)\n# Total Accepted: 24.8K\n# Total Sub... | [
0
] |
from flask import Flask
import os
app = Flask(__name__)
@app.route("/healthz")
def healthz():
return "ok"
@app.route("/alive")
def alive():
return "ok"
@app.route("/hello")
# def healthz(): # introduces application crash bug
def hello():
myhost = os.uname()[1]
body = ("V1 - Hello World! - %s" % m... | normal | {
"blob_id": "0259fddbe3ce030030a508ce7118a6a03930aa51",
"index": 7375,
"step-1": "<mask token>\n\n\n@app.route('/healthz')\ndef healthz():\n return 'ok'\n\n\n@app.route('/alive')\ndef alive():\n return 'ok'\n\n\n@app.route('/hello')\ndef hello():\n myhost = os.uname()[1]\n body = 'V1 - Hello World! -... | [
3,
4,
5,
6,
7
] |
class Solution(object):
def restoreIpAddresses(self, s):
"""
:type s: str
:rtype: List[str]
"""
def helper(sb, string, level):
if len(string) == 0:
if level == 4:
ans.append(sb[:-1])
return
if level ... | normal | {
"blob_id": "ec4348c61cd1c9130543bb20f9ca199399e1caff",
"index": 226,
"step-1": "class Solution(object):\n def restoreIpAddresses(self, s):\n \"\"\"\n :type s: str\n :rtype: List[str]\n \"\"\"\n\n def helper(sb, string, level):\n if len(string) == 0:\n ... | [
0
] |
from typing import Union, Tuple
import numpy as np
from UQpy.utilities.kernels.baseclass.GrassmannianKernel import GrassmannianKernel
class ProjectionKernel(GrassmannianKernel):
def __init__(self, kernel_parameter: Union[int, float] = None):
"""
:param kernel_parameter: Number of independent p-... | normal | {
"blob_id": "14ce803e3deb529b489c150c7ecc702118448acb",
"index": 9022,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass ProjectionKernel(GrassmannianKernel):\n <mask token>\n\n def element_wise_operation(self, xi_j: Tuple) ->float:\n \"\"\"\n Compute the Projection kernel entr... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class EndpointsUnitTests(unittest.TestCase):
<|reserved_special_token_0|>
@patch('github_approval_checker.utils.util.verify_signature')
@patch('github_approval_checker.api.endpoints.connexion')
@patch('github_approval_checker.api.endpoints.GithubHandler')
@patch('gith... | flexible | {
"blob_id": "7626202d1e3ec7321addbb028be2275b882efda2",
"index": 6453,
"step-1": "<mask token>\n\n\nclass EndpointsUnitTests(unittest.TestCase):\n <mask token>\n\n @patch('github_approval_checker.utils.util.verify_signature')\n @patch('github_approval_checker.api.endpoints.connexion')\n @patch('githu... | [
5,
6,
7,
8,
9
] |
<|reserved_special_token_0|>
def show_im(dataset):
data = np.uint8(dataset[0]).reshape((30, 96)) * 255
im = Image.fromarray(data)
im.show()
def test_model(captcha):
im = Image.open(os.path.join(basedir, 'downloader', 'captchas', captcha))
im = im.convert('L')
im = grey_to_binary(im)
im =... | flexible | {
"blob_id": "8e34b5e15c5b6107d6841e7b567abf967c631f1b",
"index": 7440,
"step-1": "<mask token>\n\n\ndef show_im(dataset):\n data = np.uint8(dataset[0]).reshape((30, 96)) * 255\n im = Image.fromarray(data)\n im.show()\n\n\ndef test_model(captcha):\n im = Image.open(os.path.join(basedir, 'downloader', ... | [
2,
3,
4,
5,
6
] |
import logging
from utils import Utils
from block import Block
from message import Message
from transaction import Transaction
class Response:
def __init__(self, node, data):
self.node = node
self.data = data
self.selector()
def selector(self):
if self.data['flag'] == 1:
... | normal | {
"blob_id": "55b8590410bfe8f12ce3b52710238a79d27189a7",
"index": 5125,
"step-1": "<mask token>\n\n\nclass Response:\n <mask token>\n <mask token>\n\n def chain_size(self):\n server_chain_size = self.node.get_ledger_size()\n self.return_response(1, server_chain_size)\n\n def chain_sync(s... | [
5,
6,
8,
9,
10
] |
'''
!pip install wget
from zipfile import ZipFile
import wget
print('Beginning file downlaod with wget module')
url = 'https://download.microsoft.com/download/3/E/1/3E1C3F21-ECDB-4869-8368-6DEBA77B919F/kagglecatsanddogs_3367a.zip'
wget.download(url, 'sample_data/')
print('2. Extract all files in ZIP to different dir... | normal | {
"blob_id": "13c9f0f58ec6da317c3802f594bb0db7c275dee9",
"index": 21,
"step-1": "<mask token>\n\n\ndef create_training_data():\n for category in CATEGORIES:\n path = os.path.join(DATADIR, category)\n classIndex = CATEGORIES.index(category)\n for img in os.listdir(path):\n try:\n... | [
1,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
if __name__ == '__main__':
fix_extra_refs(currentAddress)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
from utils.references import *
if __name__ == '__main__':
fix_extra_refs(currentAddress)
<|reserved_s... | flexible | {
"blob_id": "30a57197e3156023ac9a7c4a5218bfe825e143d9",
"index": 5978,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif __name__ == '__main__':\n fix_extra_refs(currentAddress)\n",
"step-3": "<mask token>\nfrom utils.references import *\nif __name__ == '__main__':\n fix_extra_refs(currentAddress... | [
0,
1,
2,
3
] |
<|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": "1e853d58c2066f3fbd381d0d603cd2fcece0cf15",
"index": 7933,
"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 = [('travels', '... | [
0,
1,
2,
3,
4
] |
import pandas as pd
import numpy as np
import inspect
from script.data_handler.Base.df_plotterMixIn import df_plotterMixIn
from script.util.MixIn import LoggerMixIn
from script.util.PlotTools import PlotTools
DF = pd.DataFrame
Series = pd.Series
class null_clean_methodMixIn:
@staticmethod
def drop_col(df: D... | normal | {
"blob_id": "198beb5a17575d781f7bce1ab36a6213ad7331b3",
"index": 5853,
"step-1": "<mask token>\n\n\nclass Base_dfCleaner(LoggerMixIn, null_clean_methodMixIn, df_plotterMixIn):\n <mask token>\n <mask token>\n\n def __init__(self, df: DF, df_Xs_keys, df_Ys_key, silent=False, verbose=0):\n LoggerMix... | [
8,
15,
16,
17,
19
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
while True:
ret, square = result.read()
area = square[100:200, 100:200]
cv2.imshow('video', square)
cv2.imshow('video2', area)
print(square)
if cv2.waitKey(25) & 255 == ord('q'):
break
result.releas... | flexible | {
"blob_id": "934921b22d036bd611134ce74f6eba3a2710018e",
"index": 529,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwhile True:\n ret, square = result.read()\n area = square[100:200, 100:200]\n cv2.imshow('video', square)\n cv2.imshow('video2', area)\n print(square)\n if cv2.waitKey(25... | [
0,
1,
2,
3,
4
] |
"""Note: AWS Glue split from spark since it requires different test dependencies."""
from tests.integration.backend_dependencies import BackendDependencies
from tests.integration.integration_test_fixture import IntegrationTestFixture
aws_glue_integration_tests = []
deployment_patterns = [
# TODO: The AWS_GLUE dep... | normal | {
"blob_id": "e288403cb310bb7241b25e74d1b5bcc63967128c",
"index": 1031,
"step-1": "<mask token>\n",
"step-2": "<mask token>\naws_glue_integration_tests += deployment_patterns\n",
"step-3": "<mask token>\naws_glue_integration_tests = []\ndeployment_patterns = [IntegrationTestFixture(name=\n 'how_to_use_grea... | [
0,
1,
2,
3,
4
] |
'''OpenGL extension EXT.YUV_target
This module customises the behaviour of the
OpenGL.raw.GLES2.EXT.YUV_target to provide a more
Python-friendly API
Overview (from the spec)
This extension adds support for three new YUV related items: first
rendering to YUV images, second sampling from YUV images while keeping ... | normal | {
"blob_id": "08420d31713859946b2f19cebf68c333331cb80e",
"index": 1494,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef glInitYuvTargetEXT():\n \"\"\"Return boolean indicating whether this extension is available\"\"\"\n from OpenGL import extensions\n return extensions.hasGLExtension(_EXTE... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
print(anios * dias_por_anio * horas_por_dia * segundos_por_hora)
<|reserved_special_token_1|>
anios = 30
dias_por_anio = 365
horas_por_dia = 24
segundos_por_hora = 60
print(anios * dias_por_anio * horas_por_dia * segundos_por_h... | flexible | {
"blob_id": "f153da7e4537f807f6c9d9d268a00443933d8315",
"index": 4167,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nprint(anios * dias_por_anio * horas_por_dia * segundos_por_hora)\n",
"step-3": "anios = 30\ndias_por_anio = 365\nhoras_por_dia = 24\nsegundos_por_hora = 60\nprint(anios * dias_por_anio ... | [
0,
1,
2,
3
] |
from sklearn.base import BaseEstimator
class movingAverage(BaseEstimator):
'''Implements a moving average.'''
def __init__(self, lag):
self.lag = lag
def movingAverage(self, periods=5):
'''Implements a naiveLV forecast.'''
try:
# sets data
x = self.data['Values']
d = ... | normal | {
"blob_id": "f4e45c19105d4ee1520acc0cd61dadfe27904d0f",
"index": 8134,
"step-1": "from sklearn.base import BaseEstimator\n\n\nclass movingAverage(BaseEstimator):\n '''Implements a moving average.'''\n\n def __init__(self, lag):\n self.lag = lag\n\ndef movingAverage(self, periods=5):\n '''Implemen... | [
0
] |
import numpy as np
import numdifftools as nd
from scipy import stats
from scipy import optimize
from functools import partial
class TCRPowerCalculator:
def __init__(self, pcmodel):
self.pcmodel = pcmodel
self.predict_variance = self.pcmodel.predict_variance
self.predict_mean = self.pcmodel.predict_mean
self.... | normal | {
"blob_id": "d327151c9659078e12e4aca46631de33e7ca4dcf",
"index": 167,
"step-1": "<mask token>\n\n\nclass TCRPowerCalculator:\n <mask token>\n\n def predict_detection_probability_2step(self, tcr_frequency, num_reads,\n num_cells, detect_thresh=1):\n \"\"\"\n\t\t2-step detection probability mod... | [
3,
4,
5,
6,
7
] |
s = input()
if len(s) < 26:
for i in range(26):
c = chr(ord("a")+i)
if c not in s:
print(s+c)
exit()
else:
for i in reversed(range(1,26)):
if s[i-1] < s[i]:
s1 = s[0:i-1]
for j in range(26):
c = chr(ord("a")+j)
... | normal | {
"blob_id": "9931fc25118981bcce80cffd3fda9dc99d951bf5",
"index": 180,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nif len(s) < 26:\n for i in range(26):\n c = chr(ord('a') + i)\n if c not in s:\n print(s + c)\n exit()\nelse:\n for i in reversed(range(1, 26)):\n... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
loops(loop, phoneNumber, message)
<|reserved_special_token_1|>
<|reserved_special_token_0|>
phoneNumber = 'fill the number'
message = 'fill with ur message'
loop = 1
loops(loop, phoneNumber, message)
<|reserved_special_token_... | flexible | {
"blob_id": "81dfdf0479fc1f136fa5153840d8c7015f9db676",
"index": 32,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nloops(loop, phoneNumber, message)\n",
"step-3": "<mask token>\nphoneNumber = 'fill the number'\nmessage = 'fill with ur message'\nloop = 1\nloops(loop, phoneNumber, message)\n",
"step-4... | [
0,
1,
2,
3,
4
] |
# 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 applicable law or agreed to ... | normal | {
"blob_id": "5e2fcc6379a8ecee0378d26108e4deab9d17dba6",
"index": 7483,
"step-1": "<mask token>\n\n\nclass NSDescriptorsViewSet(viewsets.ModelViewSet):\n <mask token>\n <mask token>\n <mask token>\n\n def get_success_headers(self, data):\n return {'Location': data['_links']['self']}\n\n def ... | [
6,
7,
8,
9,
10
] |
class Reader:
@staticmethod
def read_file(file_path):
return ''
| normal | {
"blob_id": "8c51b2c06f971c92e30d6b2d668fdd2fd75142d2",
"index": 4846,
"step-1": "<mask token>\n",
"step-2": "class Reader:\n <mask token>\n",
"step-3": "class Reader:\n\n @staticmethod\n def read_file(file_path):\n return ''\n",
"step-4": null,
"step-5": null,
"step-ids": [
0,
... | [
0,
1,
2
] |
import tensorflow as tf
import blood_model
import os
import numpy as np
FLAGS = tf.app.flags.FLAGS
RUN = 'new_test_hm'
tf.app.flags.DEFINE_string('checkpoint_dir', RUN+'/checkpoints',
"""Directory where to write event logs and checkpoint.""")
tf.app.flags.DEFINE_string('summaries_dir', RUN+... | normal | {
"blob_id": "f653e906d3026de4bb1e705162f4321bb75e8705",
"index": 4166,
"step-1": "<mask token>\n\n\ndef check_filesystem():\n \"\"\"\n either start a new checkpoint or continue from existing checkpoint folder\n \"\"\"\n if FLAGS.continue_run:\n if not tf.gfile.Exists(FLAGS.summaries_dir):\n ... | [
3,
5,
6,
7,
8
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
while True:
print('Light Levels:' + input.light_level())
if input.light_level() < 6:
light.set_all(light.rgb(255, 0, 255))
elif input.light_level() < 13:
light.set_all(light.rgb(255, 0, 0))
else:
light.clear()
<|reserved_spec... | flexible | {
"blob_id": "7277b045f85d58383f26ab0d3299feb166f45e36",
"index": 2575,
"step-1": "<mask token>\n",
"step-2": "while True:\n print('Light Levels:' + input.light_level())\n if input.light_level() < 6:\n light.set_all(light.rgb(255, 0, 255))\n elif input.light_level() < 13:\n light.set_all(... | [
0,
1,
2
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
parser.add_argument('data_dir', help='path to training images')
parser.add_argument('--save_dir', default='.', help=
'path where checkpoint is saved')
parser.add_argument('--arch', default='vgg11', help=
'which pre-trained... | flexible | {
"blob_id": "0c3947a1699c78080661a55bbaa9215774b4a18e",
"index": 4751,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nparser.add_argument('data_dir', help='path to training images')\nparser.add_argument('--save_dir', default='.', help=\n 'path where checkpoint is saved')\nparser.add_argument('--arch',... | [
0,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
def target2_text(first_input, *params):
return first_input
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
def input2_text(first_input, *params):
return my_dataset.voc.idx2docs(first_input)
def target2_text(first_input, *params):
... | flexible | {
"blob_id": "77884dd72f5efe91fccad27e6328c4ad34378be2",
"index": 6953,
"step-1": "<mask token>\n\n\ndef target2_text(first_input, *params):\n return first_input\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef input2_text(first_input, *params):\n return my_dataset.voc.idx2docs(first_input)\n\n\ndef... | [
1,
2,
3,
4,
5
] |
<|reserved_special_token_0|>
class RSTTable(Table):
def run(self) ->List[Node]:
...
class CSVTable(Table):
option_spec: Dict[str, Callable[[str], Any]] = ...
class DocutilsDialect(csv.Dialect):
delimiter: str = ...
quotechar: str = ...
doublequote: bool = ...
s... | flexible | {
"blob_id": "9abf2b9b90d18332ede94cf1af778e0dda54330b",
"index": 949,
"step-1": "<mask token>\n\n\nclass RSTTable(Table):\n\n def run(self) ->List[Node]:\n ...\n\n\nclass CSVTable(Table):\n option_spec: Dict[str, Callable[[str], Any]] = ...\n\n\n class DocutilsDialect(csv.Dialect):\n delim... | [
11,
19,
20,
22,
24
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open('sample.csv') as rf:
csv_reader = csv.DictReader(rf)
with open('sample1.csv', 'w') as wf:
csv_headers = ['fname', 'lname', 'email']
if os.path.isfile('sample1.csv'):
q = input('File al... | flexible | {
"blob_id": "43196258b61801799b8d6b7d23f5816d84cb5dff",
"index": 7294,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open('sample.csv') as rf:\n csv_reader = csv.DictReader(rf)\n with open('sample1.csv', 'w') as wf:\n csv_headers = ['fname', 'lname', 'email']\n if os.path.isfile... | [
0,
1,
2,
3
] |
# Licensed under a 3-clause BSD style license - see LICENSE.rst
"""
This is a collection of monkey patches and workarounds for bugs in
earlier versions of Numpy.
"""
from ...utils import minversion
__all__ = ['NUMPY_LT_1_10_4', 'NUMPY_LT_1_11',
'NUMPY_LT_1_12', 'NUMPY_LT_1_13', 'NUMPY_LT_1_14',
... | normal | {
"blob_id": "9376d697158faf91f066a88e87d317e79a4d9240",
"index": 6575,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n__all__ = ['NUMPY_LT_1_10_4', 'NUMPY_LT_1_11', 'NUMPY_LT_1_12',\n 'NUMPY_LT_1_13', 'NUMPY_LT_1_14', 'NUMPY_LT_1_14_1', 'NUMPY_LT_1_14_2']\nNUMPY_LT_1_10_4 = not minversion('numpy', '1.... | [
0,
1,
2,
3
] |
<|reserved_special_token_0|>
class DummyWorker:
def echo(self, message='hello', delay=0, fail=False):
sleep(delay)
if fail:
raise Exception('failed')
self.message = message
return self.message
def test_default(working_path):
task = Task(DummyWorker(), 'echo').sta... | flexible | {
"blob_id": "d2e46944ab05c5e8c1979101728b7b25900be342",
"index": 415,
"step-1": "<mask token>\n\n\nclass DummyWorker:\n\n def echo(self, message='hello', delay=0, fail=False):\n sleep(delay)\n if fail:\n raise Exception('failed')\n self.message = message\n return self.me... | [
4,
5,
7,
9,
10
] |
"""
Sprites - animations for objects.
"""
import config
import os
import pygame
class Sheet(object):
""" An single large image composed of smaller images used for sprite
animations. All the sprites on the sheet must be the same size. The width x
height give the sprite dimensions in pixels. The rows x colu... | normal | {
"blob_id": "080aa8b99cdded7a947880a1c3399f68b28ae44d",
"index": 6318,
"step-1": "<mask token>\n\n\nclass Sprite(object):\n <mask token>\n <mask token>\n <mask token>\n <mask token>\n\n\nclass CompositeSprite(Sprite):\n \"\"\" A sprite that is composed of multiples sprites layered on top of each\n... | [
16,
18,
19,
21,
26
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
with open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:
long_description = f.read()
setup(name='SumoSound', packages=['SumoSound'], version='1.0.2', license=
'MIT', description=
'A python library to a... | flexible | {
"blob_id": "81c9cabaa611f8e884708d535f0b99ff83ec1c0d",
"index": 8319,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(path.join(this_directory, 'README.md'), encoding='utf-8') as f:\n long_description = f.read()\nsetup(name='SumoSound', packages=['SumoSound'], version='1.0.2', license=\n ... | [
0,
1,
2,
3,
4
] |
<|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_0|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
class Migration(migrations.... | flexible | {
"blob_id": "d40e1cfa2ef43f698e846c25ac9f5471d69e71a0",
"index": 5253,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Migration(migrations.Migration):\n <mask token>\n <mask token>\n <mask token>\n",
"step-3": "<mask token>\n\n\nclass Migration(migrations.Migration):\n initial = T... | [
0,
1,
2,
3,
4
] |
from context import vicemergencyapi
from vicemergencyapi.vicemergency import VicEmergency
from geographiclib.geodesic import Geodesic
from shapely.geometry import Point
def geoDistance(p1, p2):
return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']
melbourne = Point(144.962272, -37.812274)
def compare(f... | normal | {
"blob_id": "920f00632599945397364dd0f52f21234e17f9ef",
"index": 9445,
"step-1": "<mask token>\n\n\ndef geoDistance(p1, p2):\n return Geodesic.WGS84.Inverse(p1.y, p1.x, p2.y, p2.x)['s12']\n\n\n<mask token>\n\n\ndef compare(f):\n return geoDistance(f.getLocation(), melbourne)\n\n\n<mask token>\n",
"step-2... | [
2,
3,
4,
5,
6
] |
from . import match
from . import mimetype
from .mimetype import MIMEType
def sniff_unknown(resource: bytes, sniff_scriptable: bool = False): #might need more arguments
raise NotImplementedError
def sniff_mislabeled_binary(resource: bytes) -> MIMEType:
raise NotImplementedError
def sniff_mislabeled_feed(reso... | normal | {
"blob_id": "a2344f405aa681daff12166b7aad1230652373de",
"index": 3499,
"step-1": "<mask token>\n\n\ndef sniff_mislabeled_feed(resource: bytes) ->MIMEType:\n raise NotImplementedError\n\n\ndef sniff(resource: bytes, mime_type_string: str='unknown/unknown',\n no_sniff: bool=False, check_for_apache_bug: bool=... | [
2,
3,
4,
5,
6
] |
<|reserved_special_token_0|>
<|reserved_special_token_1|>
from .exec_generator import *
| flexible | {
"blob_id": "b6ee3c980357ab22a7969c21207b34546c87092d",
"index": 7305,
"step-1": "<mask token>\n",
"step-2": "from .exec_generator import *\n",
"step-3": null,
"step-4": null,
"step-5": null,
"step-ids": [
0,
1
]
} | [
0,
1
] |
import time
import random
from BlockchainNetwork.MVB import *
from threading import Thread
coloredlogs.install()
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(name)s - %(levelname)s - %(message)s')
log = logging.getLogger(__name__)
class MVBTest:
def __init__(self, initialNodeCnt):
sel... | normal | {
"blob_id": "8ad9efbbb2d9e2a5f73ebbb999da3ed93e4c1974",
"index": 9655,
"step-1": "<mask token>\n\n\nclass MVBTest:\n <mask token>\n <mask token>\n\n def doubleSpendTest(self):\n \"\"\"\n txOutputs is the genesis output.\n txOutputs[0] was used twice in this test.\n ... | [
11,
15,
17,
18,
19
] |
import json
from typing import TYPE_CHECKING
import pytest
from eth_utils import is_checksum_address
from rotkehlchen.globaldb.handler import GlobalDBHandler
from rotkehlchen.types import ChainID
if TYPE_CHECKING:
from rotkehlchen.chain.ethereum.node_inquirer import EthereumInquirer
def test_evm_contracts_data... | normal | {
"blob_id": "52dc8a4f9165a88dddc1da16e0adb045c4d851ed",
"index": 5017,
"step-1": "<mask token>\n\n\ndef test_evm_contracts_data(globaldb):\n \"\"\"Test that all evm contract entries in the packaged global DB have legal data\"\"\"\n serialized_chain_ids = [x.serialize_for_db() for x in ChainID]\n with gl... | [
2,
3,
4,
5,
6
] |
filename = 'learning_python.txt'
# with open(filename) as file_object:
# contents = file_object.read()
# print(contents)
# with open(filename) as file_object:
# for line in file_object:
# print(line.rstrip())
with open(filename) as file_object:
lines = file_object.readlines()
c_string = ''
for line in lines:
... | normal | {
"blob_id": "2f0dc8697e979f307c86a08832b0eae86357d416",
"index": 2497,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nwith open(filename) as file_object:\n lines = file_object.readlines()\n<mask token>\nfor line in lines:\n c_string += line.rstrip()\nprint(f\"{c_string.replace('Python', 'Scala')}\"... | [
0,
1,
2,
3
] |
"""
Given an array nums and a value val, remove all instances of
that value in-place and return the new length.
Do not allocate extra space for another array, you must do
this by modifying the input array in-place with O(1) extra memory.
The order of elements can be changed. It doesn't matter
... | normal | {
"blob_id": "8be4bf5c1a5a7b841edc915793571686ee0bffe6",
"index": 113,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\nclass Solution:\n <mask token>\n\n\n<mask token>\n",
"step-3": "<mask token>\n\n\nclass Solution:\n\n def remove_element(self, nums: list[int], val: int) ->int:\n last_p... | [
0,
1,
2,
3
] |
#!/usr/bin/env python
from ks_auth import sess
from ks_auth import trust_auth
from ks_auth import ks
from ks_auth import utils
from novaclient import client
import novaclient.exceptions
from time import sleep
from uuid import uuid4
import sys
# RDTIBCC-1042
VERSION = '2'
nova = client.Client(VERSION, session=sess)
u... | normal | {
"blob_id": "9f40162348d33d70639692dac87777a2799999e9",
"index": 6688,
"step-1": "#!/usr/bin/env python\nfrom ks_auth import sess\nfrom ks_auth import trust_auth\nfrom ks_auth import ks\nfrom ks_auth import utils\nfrom novaclient import client\nimport novaclient.exceptions\nfrom time import sleep\nfrom uuid impo... | [
0
] |
from ._monitor import TMonitor as TMonitor, TqdmSynchronisationWarning as TqdmSynchronisationWarning
from ._tqdm_pandas import tqdm_pandas as tqdm_pandas
from .cli import main as main
from .gui import tqdm as tqdm_gui, trange as tgrange
from .std import TqdmDeprecationWarning as TqdmDeprecationWarning, TqdmExperimental... | normal | {
"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|>
<|reserved_special_token_1|>
<|reserved_special_token_0|>
urlpatterns = [path('', HomeView.as_view(), name='HomeView'), path(
'LoginView/', LoginView.as_view(), name='LoginView'), path(
'SignUpView/', SignUpView.as_view(), name='SignUpView'), path(
'SettingsView/', SettingsVi... | flexible | {
"blob_id": "5bd8cee2595215fda6ab523a646cf918e3d84a50",
"index": 937,
"step-1": "<mask token>\n",
"step-2": "<mask token>\nurlpatterns = [path('', HomeView.as_view(), name='HomeView'), path(\n 'LoginView/', LoginView.as_view(), name='LoginView'), path(\n 'SignUpView/', SignUpView.as_view(), name='SignUpV... | [
0,
1,
2,
3
] |
import json
from asgiref.sync import async_to_sync
from daphne_API.diversifier import activate_diversifier
from daphne_API.models import Design
def send_archs_back(channel_layer, channel_name, archs):
async_to_sync(channel_layer.send)(channel_name,
{
... | normal | {
"blob_id": "564c613491b0d1797b216a0bd425690e9fae12bc",
"index": 7725,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\ndef send_archs_from_queue_to_main_dataset(context):\n background_queue_qs = Design.objects.filter(activecontext_id__exact=\n context.eosscontext.activecontext.id)\n arch_... | [
0,
1,
2,
3,
4
] |
import sys
import pytest
from presidio_evaluator.evaluation import Evaluator
from tests.conftest import assert_model_results_gt
from presidio_evaluator.models.flair_model import FlairModel
@pytest.mark.slow
@pytest.mark.skipif("flair" not in sys.modules, reason="requires the Flair library")
def test_flair_simple(sm... | normal | {
"blob_id": "813d27e8f9c1a416dab2f891dd71e4791bb92dbb",
"index": 1040,
"step-1": "<mask token>\n",
"step-2": "<mask token>\n\n\n@pytest.mark.slow\n@pytest.mark.skipif('flair' not in sys.modules, reason=\n 'requires the Flair library')\ndef test_flair_simple(small_dataset):\n flair_model = FlairModel(mode... | [
0,
1,
2,
3
] |
import cv2 as cv
def nothing(x):
pass
cv.namedWindow('Binary')
cv.createTrackbar('threshold', 'Binary', 0, 255, nothing)
cv.setTrackbarPos('threshold', 'Binary', 127)
img_color = cv.imread('../sample/ball.png', cv.IMREAD_COLOR)
img_gray = cv.cvtColor(img_color, cv.COLOR_BGR2GRAY)
while(True):
thre = cv.get... | normal | {
"blob_id": "034d4027ea98bca656178b66c5c6e6e8b13e4b9e",
"index": 4219,
"step-1": "<mask token>\n\n\ndef nothing(x):\n pass\n\n\n<mask token>\n",
"step-2": "<mask token>\n\n\ndef nothing(x):\n pass\n\n\ncv.namedWindow('Binary')\ncv.createTrackbar('threshold', 'Binary', 0, 255, nothing)\ncv.setTrackbarPos(... | [
1,
2,
3,
4,
5
] |
#!/usr/bin/env python
'''
Script for analysis of wavefunctions on GaSb/InAs/GaSb simmetric quantum wells.
This piece code is part of the project "phd_gasb_inas", which comprises the work
related to the Phd. Dissertation named: "Quantum transport of charge and spin in
topological insulators 2D".
Author: Marcos Medeiro... | normal | {
"blob_id": "a012055d11202c68d9eddf5cf2a17043f9bbaf0a",
"index": 6851,
"step-1": "<mask token>\n\n\ndef map_density(ax, syst, psi_sqrd, colormap='Reds'):\n kwant.plotter.map(syst, psi_sqrd, ax=ax, fig_size=(7, 3), cmap=colormap,\n vmax=0.99 * max(psi_sqrd))\n tools.edit_axis(ax, 'dens')\n return ... | [
4,
5,
7,
8,
9
] |
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