added stringdate 2024-11-18 17:59:49 2024-11-19 03:44:43 | created int64 0 2,086B | id stringlengths 40 40 | int_score int64 2 5 | metadata dict | score float64 2.31 5.34 | source stringclasses 1
value | text stringlengths 259 23.5k | num_lines int64 16 648 | avg_line_length float64 15 60.9 | max_line_length int64 31 179 | ast_depth int64 8 40 | length int64 101 3.8k | lang stringclasses 1
value | sast_codeql_findings stringlengths 2 276k | sast_codeql_findings_count int64 0 33 | sast_codeql_success bool 1
class | sast_codeql_error stringclasses 1
value |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
2024-11-19T02:04:21.038054+00:00 | 1,579,081,657,000 | 165a9c8d541c3eaf91bb3387e19cf86f2d7574f2 | 2 | {
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"MIT"
],
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"extension": "py",
"fi... | 2.5 | stackv2 | from typing import Optional, Iterable
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
from svae import RNN_TYPES
from svae.utils.modules import ResidualBlock
class RNNDecoder(nn.Module):
def __init__(self,
rnn_type: str = 'gru',
... | 67 | 33.82 | 72 | 14 | 536 | python | [] | 0 | true | |
2024-11-19T02:04:21.612783+00:00 | 1,582,050,116,000 | 63740476baddb91c9f41a0a1cad288062ad9d008 | 3 | {
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"MIT"
],
"directory_id": "4d827c10251027136c216c2518b05ba9a3098c9a",
"extension": "py",
"fi... | 2.78125 | stackv2 | from typing import List
from .common import LOOKUP_TABLE, S_BOX
Word = bytes
def key_expension(key: bytes, rounds: int) -> List[Word]:
if len(key) != 16:
raise ValueError(f"Wrong key length: {len(key)}")
expended_key = [key[i : i + len(key) // 4] for i in range(0, len(key), 4)]
for r in range(1,... | 62 | 31.15 | 79 | 15 | 590 | python | [] | 0 | true | |
2024-11-19T02:04:22.201888+00:00 | 1,565,991,100,000 | 3dce1bd0dd8b57e326725981db4c373b578f43c0 | 3 | {
"blob_id": "3dce1bd0dd8b57e326725981db4c373b578f43c0",
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"Apache-2.0"
],
"directory_id": "25feabaab181efff592a20a5e3397909d3bc8efd",
"extension": "py"... | 2.6875 | stackv2 | # --------------------------------------------------------------------------------------------------
# Name: Layer Manager
# Purpose: Manages and displays current Sprite's layers;
#
# Author: Rafael Vasco
#
# Created: 04/08/2013
# Copyright: (c) Rafael 2013
# Licence: <your licence>
#---------... | 204 | 24.61 | 100 | 16 | 1,106 | python | [] | 0 | true | |
2024-11-19T02:04:22.285531+00:00 | 1,451,761,496,000 | bf15ff8f57bf045c379146d83cc77f21b1817aa9 | 3 | {
"blob_id": "bf15ff8f57bf045c379146d83cc77f21b1817aa9",
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"BSD-3-Clause"
],
"directory_id": "b52a30e38f517091fa17ede66d5dcc8649bd0c14",
"extension": "p... | 2.734375 | stackv2 |
"""
datasets.py
===========
Access geographical datasets.
Commands
--------
list_datasets() : get a list of available datasets.
describe_dataset(dataset_name) : get metadata on specified
dataset.
load_dataset(dataset_name) : load dataset. Returns a numpy array.
"""
import json
import os
import ... | 42 | 21.14 | 69 | 10 | 210 | python | [] | 0 | true | |
2024-11-19T02:04:22.456196+00:00 | 1,594,063,464,000 | 632b400f03924612cf333b7add235b5dd1bf2215 | 2 | {
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"detected_licenses": [
"MIT"
],
"directory_id": "d80d822dfbfede6a113374b2cc2139112b5f68a5",
"extension": "py",
"fi... | 2.4375 | stackv2 | import ast
import util
import joblib
import numpy as np
SAVED_MODEL = joblib.load(util.MODEL_PATH)
def predict(x):
try:
x = ast.literal_eval(x)
pred = SAVED_MODEL.predict(np.array(x))
return {"prices": pred.tolist()}
except (ValueError, SyntaxError):
return (
"Inv... | 22 | 22.32 | 57 | 12 | 127 | python | [] | 0 | true | |
2024-11-19T02:04:23.389605+00:00 | 1,628,718,996,000 | e7fd77d918f23e9a318c9af8a3b3864409ec225e | 3 | {
"blob_id": "e7fd77d918f23e9a318c9af8a3b3864409ec225e",
"branch_name": "refs/heads/master",
"committer_date": 1628718996000,
"content_id": "20997b76cc331210d93113b770155d75970756d9",
"detected_licenses": [
"MIT"
],
"directory_id": "8c66d6cc13ee2627f9c572bbd24e53065c6b2c39",
"extension": "py",
"fi... | 2.515625 | stackv2 | """
Flask app factory
@date: 6/12/2018
@author: Larry Shi
"""
import os
from flask import Flask, render_template
# Constant
PROJECT_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
def create_app():
flask_app = Flask(__name__)
if 'APP_SETTINGS' in os.environ:
flask_app.config.from_... | 39 | 22.51 | 73 | 11 | 218 | python | [] | 0 | true | |
2024-11-19T02:04:23.603268+00:00 | 1,687,270,223,000 | 60496e42f1d9af87738cb5387f57030b30a6482d | 3 | {
"blob_id": "60496e42f1d9af87738cb5387f57030b30a6482d",
"branch_name": "refs/heads/main",
"committer_date": 1687270223000,
"content_id": "9f0e08587b873f0e28ba6b8335b047460f4f7971",
"detected_licenses": [
"MIT"
],
"directory_id": "361e4f53c74f58c7fd1e9eb922f4f51d1027482d",
"extension": "py",
"file... | 3.265625 | stackv2 | from enum import Enum
from typing import List
class Location:
def __init__(self, name: str, name_nepali: str):
self.__name = name
self.__name_nepali = name_nepali
def __str__(self):
return self.name
def __repr__(self):
return self.name
@property
def name(self):
... | 105 | 26.33 | 84 | 11 | 695 | python | [] | 0 | true | |
2024-11-19T02:04:23.728578+00:00 | 1,617,470,018,000 | 0f34b7976ca7f126c4cd39271b316a1cbe1aebe5 | 3 | {
"blob_id": "0f34b7976ca7f126c4cd39271b316a1cbe1aebe5",
"branch_name": "refs/heads/main",
"committer_date": 1617470018000,
"content_id": "c6a857295696c55e26476d25d025e7a09aa3aa57",
"detected_licenses": [
"MIT"
],
"directory_id": "7c8b5f35cad9d47b750ee26f2ee0f2701a049918",
"extension": "py",
"file... | 3.34375 | stackv2 | """
Random processes exist everywhere. Roughly speaking, a random process is a
system of related random variables, usually indexed with respect to time t ≥ 0,
for a continuous random process, or by natural numbers n = 1, 2, ..., for a
discrete random process.
This module examine the Poisson process, which is a simple ... | 45 | 29.38 | 79 | 11 | 349 | python | [] | 0 | true | |
2024-11-19T02:04:23.771587+00:00 | 1,634,484,659,000 | 367e7c33aecde46dc56f3a6839301c54f2b7e366 | 2 | {
"blob_id": "367e7c33aecde46dc56f3a6839301c54f2b7e366",
"branch_name": "refs/heads/main",
"committer_date": 1634484659000,
"content_id": "94f615705a75695f9e94af2801f00ed132fc5bf9",
"detected_licenses": [
"MIT"
],
"directory_id": "fc34331c15da63d504dcacfa3b35d6995a75e105",
"extension": "py",
"file... | 2.34375 | stackv2 | from django.db import models
from django.contrib.auth.models import BaseUserManager, \
AbstractBaseUser, PermissionsMixin
from django.core.validators import RegexValidator
from django.conf import settings
from django.utils.translation import gettext_lazy as _
class UserManager(BaseUserManager):
"""User manager f... | 105 | 33.92 | 150 | 16 | 846 | python | [] | 0 | true | |
2024-11-19T02:04:23.972583+00:00 | 1,306,516,789,000 | bcaafd1af3a72d0f6ac3071cef0f73569d3f7001 | 3 | {
"blob_id": "bcaafd1af3a72d0f6ac3071cef0f73569d3f7001",
"branch_name": "refs/heads/master",
"committer_date": 1306516789000,
"content_id": "3222ad88819bce2deb9617a2476d98fa42417fb0",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "7068d02c0abdd0775b7e5717fea2bccec28f656b",
"extension": "py"... | 2.734375 | stackv2 | import scipy
from py2scad import *
import params
class BackPlate(object):
def __init__(self,params=params):
self.params = params
self.__make()
def __str__(self):
return self.part.__str__()
def __make(self):
# Create main plate
cap_numb = self.params.base_pcb['cap... | 61 | 30.48 | 85 | 14 | 507 | python | [] | 0 | true | |
2024-11-19T02:04:24.070791+00:00 | 1,456,750,078,000 | 093e1f0b029a40ab6760b6ba2704287ed559abea | 3 | {
"blob_id": "093e1f0b029a40ab6760b6ba2704287ed559abea",
"branch_name": "refs/heads/master",
"committer_date": 1456750078000,
"content_id": "4d42c177b7302f6834fbf293bd6c10a7eaa50a00",
"detected_licenses": [
"MIT"
],
"directory_id": "5e9257762ce6bec4f13bac4b0007d1227f3c949e",
"extension": "py",
"fi... | 2.734375 | stackv2 | from matplotlib import pyplot as plt
from matplotlib import animation
import numpy as np
import yaml
class BoidFlock(object):
def __init__(self, config = 0):
if config == 0:
#if no config file, default values
self.number = 50
self.lowlim = lowerposlimit = np.array([-450.0,300.0])
self.uplim = up... | 177 | 26.65 | 93 | 19 | 1,411 | python | [] | 0 | true | |
2024-11-19T02:04:24.271957+00:00 | 1,667,412,759,000 | 1875d1eac01479c649aba6575fb1552bf1a54fae | 3 | {
"blob_id": "1875d1eac01479c649aba6575fb1552bf1a54fae",
"branch_name": "refs/heads/master",
"committer_date": 1667412759000,
"content_id": "76c007982229544adfed281c8fc41a0b16e992b1",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "8eae734c653a47fd88bc4c07fbc1d7693155e2cc",
"extension": "p... | 2.609375 | stackv2 | import goodtiming.core.i18n
from goodtiming.core.parser import CompositeParser
from goodtiming.core.processor import CompositeProcessor
from goodtiming.core.renderer import CompositeRenderer
import goodtiming.core.database
import goodtiming.modules.addtodo
import goodtiming.modules.reporttiming
import goodtiming.mod... | 53 | 35.7 | 166 | 13 | 391 | python | [] | 0 | true | |
2024-11-19T02:04:24.388324+00:00 | 1,622,457,171,000 | a4196bf50086e9b5d3acd1e3513b0db48d727479 | 3 | {
"blob_id": "a4196bf50086e9b5d3acd1e3513b0db48d727479",
"branch_name": "refs/heads/master",
"committer_date": 1622457171000,
"content_id": "b07da73fc77bf6ea3e23440451194f23f035684a",
"detected_licenses": [
"MIT"
],
"directory_id": "84a19fe0b89bb19caa1641aeadc9623c1a181767",
"extension": "py",
"fi... | 3.109375 | stackv2 | from fractions import gcd
from itertools import accumulate
# 入力
N = int(input())
A = list(map(int, input().split()))
# 先頭から連続する要素の最大公約数の列
L = [0] + list(accumulate(A, gcd))
# 末尾から連続する要素の最大公約数の列
R = [0] + list(accumulate(reversed(A), gcd))
# Aの各要素について、それを使用しない場合の最大公約数の最大値をL, Rを用いて求める
ans = max(
gcd(L[k], R[-(k + ... | 20 | 17.3 | 44 | 12 | 155 | python | [] | 0 | true | |
2024-11-19T02:04:24.781765+00:00 | 1,580,473,755,000 | 79e18c08407b8393e2cc3b35e8f83de457e86589 | 3 | {
"blob_id": "79e18c08407b8393e2cc3b35e8f83de457e86589",
"branch_name": "refs/heads/master",
"committer_date": 1580473755000,
"content_id": "98383d212a2e3e17fb7f5db96acd5a20f4cc5bbf",
"detected_licenses": [
"MIT"
],
"directory_id": "360ca176a67f5333f75f1737efb4df2b5966c453",
"extension": "py",
"fi... | 2.5625 | stackv2 | from typing import List, Any, Tuple
import random
import string
import time
import statistics
import torch
from pytorch_fast_elmo import batch_to_char_ids, FastElmo
class SentenceGenerator:
def __init__(
self,
word_min: int,
word_max: int,
sent_min: int,
... | 123 | 24.71 | 87 | 16 | 713 | python | [] | 0 | true | |
2024-11-19T02:04:24.842002+00:00 | 1,602,577,796,000 | e97b4164749745bcced6590953ca710fb6fa9862 | 2 | {
"blob_id": "e97b4164749745bcced6590953ca710fb6fa9862",
"branch_name": "refs/heads/master",
"committer_date": 1602577796000,
"content_id": "0de5fc2438c469231ab0dec6d29c509cef066026",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "0ad009211f492645b5441c048b57e7abe5095a88",
"extension": "py"... | 2.34375 | stackv2 | import torch
import torch.nn as nn
import torch.optim as optim
import numpy as np
from torchtext.data import BucketIterator
import random
import math
import time
# Import Data Pre-process Files and Config File
from Model.CNNSeq2Seq import data_preprocess as dp
from Model.CNNSeq2Seq import config
from Model.CNNSeq2Se... | 178 | 26.7 | 112 | 16 | 1,253 | python | [] | 0 | true | |
2024-11-19T02:04:24.903805+00:00 | 1,572,690,647,000 | 78c03d86261e8d7f1b87ac7a1a12772e46e8a11d | 3 | {
"blob_id": "78c03d86261e8d7f1b87ac7a1a12772e46e8a11d",
"branch_name": "refs/heads/master",
"committer_date": 1572690647000,
"content_id": "cbc9812ef168a2ac1db4bc1ef01e24de2f7ba069",
"detected_licenses": [
"MIT"
],
"directory_id": "97eb2aba7c6299bcfbdd8fa998d35ca33988978e",
"extension": "py",
"fi... | 2.796875 | stackv2 | import json
class AST(object):
def __init__(self, ast):
self.ast = ast
def generate(self):
proc = self.get_proc(self.ast)
return proc(self.ast, 1)
def proc(self, ast, deep):
return self.get_proc(ast)(ast, deep)
def get_proc(self, ast):
key = "type_{}".format(ast["type"])
if hasattr(self, key):
r... | 192 | 24.69 | 97 | 17 | 1,406 | python | [] | 0 | true | |
2024-11-19T02:04:25.121314+00:00 | 1,586,779,918,000 | db4fccf8581f12774be6789bf37eb53ad3001256 | 3 | {
"blob_id": "db4fccf8581f12774be6789bf37eb53ad3001256",
"branch_name": "refs/heads/master",
"committer_date": 1586779918000,
"content_id": "7d6941e1a59d0e06687ac393e269b318c1e2917a",
"detected_licenses": [
"MIT"
],
"directory_id": "d6cf2862c79859674d9abd2bafe7b603ff657870",
"extension": "py",
"fi... | 2.53125 | stackv2 |
import sys
import os
import logging
from . import config
logger = logging.getLogger('carson')
_init = None
_debug = False
def initLogging(debug=False):
global _init, _debug
if _init is not None:
return
_init = True
_debug = debug
formatter = logging.Formatter('{asctime} {levelname[0]}... | 117 | 30.74 | 173 | 20 | 897 | python | [] | 0 | true | |
2024-11-19T02:04:25.333885+00:00 | 1,405,560,494,000 | 74d6f093484b8ac56ad4527fecbdcab241fa2e57 | 3 | {
"blob_id": "74d6f093484b8ac56ad4527fecbdcab241fa2e57",
"branch_name": "refs/heads/master",
"committer_date": 1405560494000,
"content_id": "fabff4b84d3dbc128fa61e23432a7d163081527f",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "31fdb98eea5b6323166d740198f9f56e78265cb7",
"extension": "py"... | 2.9375 | stackv2 | import time
import RPi.GPIO as GPIO
class GarageDoor:
RELAY1 = 18
ON = 1
OFF = 0
#Time in seconds to keep the action on.
ACTION_TIME = 0.5
def __init__(self):
self.__setup()
print "GarageDoor:Init"
def __setup(self):
GPIO.setmode(GPIO.BCM)
GPIO.output(self.RELAY1, self.ON)
GPIO.setup(self.RELAY1,... | 40 | 16.27 | 40 | 10 | 206 | python | [] | 0 | true | |
2024-11-19T02:04:25.403731+00:00 | 1,575,641,976,000 | 8b4057b390fa379bd21889d351a78c85b4f107c9 | 3 | {
"blob_id": "8b4057b390fa379bd21889d351a78c85b4f107c9",
"branch_name": "refs/heads/master",
"committer_date": 1575641976000,
"content_id": "4718766207552ff5b7910ce37ae53cee0b04571a",
"detected_licenses": [
"Unlicense"
],
"directory_id": "15998eacb7f19de6ac29fd8b3d446ce8190db35a",
"extension": "py",... | 2.671875 | stackv2 | from django.db import models
from django.contrib.auth.models import User
import uuid
import datetime
# Função auxiliar para definir o tempo default
# De uma reserva
def get_data():
return datetime.datetime.now() + datetime.timedelta(days=7)
# Classe para o model Propriedade, com campos que descrevem
# Uma propri... | 130 | 33.57 | 124 | 11 | 1,171 | python | [] | 0 | true | |
2024-11-19T02:04:25.867014+00:00 | 1,555,600,191,000 | 60521934a42f0a563468c9c6707e1bd3364981cb | 3 | {
"blob_id": "60521934a42f0a563468c9c6707e1bd3364981cb",
"branch_name": "refs/heads/master",
"committer_date": 1555600191000,
"content_id": "48b7ef07b4a323ecb61f730823fed782c07e99ca",
"detected_licenses": [
"MIT"
],
"directory_id": "dae622609b9807005bfeee31d14576916b6ce693",
"extension": "py",
"fi... | 3 | stackv2 | """
Transformations and stats for quantized, categorical, and collection features.
"""
import attr
import typing
import numpy as np
import warnings
from trickster.utils.counter import ExpansionCounter, CounterLimitExceededError
def expand_quantized_increment(sample, feat_idxs):
"""
Get the neighbouring val... | 316 | 27.09 | 96 | 17 | 2,020 | python | [] | 0 | true | |
2024-11-19T02:04:25.931183+00:00 | 1,395,579,750,000 | 92759c4cfade3f61b3402721f04a8abc93a6e86f | 2 | {
"blob_id": "92759c4cfade3f61b3402721f04a8abc93a6e86f",
"branch_name": "refs/heads/master",
"committer_date": 1395579750000,
"content_id": "1174760e4815f83f18d5d31829a599f1c0589d3c",
"detected_licenses": [
"MIT"
],
"directory_id": "88762da8b952258847956c4befb3648b0deb66e5",
"extension": "py",
"fi... | 2.421875 | stackv2 |
import sys
import base as New
def reload_modules(modlist):
for x in modlist:
if x in sys.modules:
reload(sys.modules[x])
print x + ' has been reloaded.'
else:
sys.modules[x] = __import__(x)
print x + ' has been loaded.'
class CustomBot(New.... | 74 | 46.85 | 122 | 16 | 791 | python | [] | 0 | true | |
2024-11-19T02:04:25.980024+00:00 | 1,557,783,992,000 | dc7d5082bb19de913d94455b11c2f99812d0bd12 | 3 | {
"blob_id": "dc7d5082bb19de913d94455b11c2f99812d0bd12",
"branch_name": "refs/heads/master",
"committer_date": 1557789613000,
"content_id": "80ea5395dea3253930825c3ecff70f64b8bc20f5",
"detected_licenses": [
"MIT"
],
"directory_id": "369c462d43cb05bce0a970c85add3203dc7fb5d2",
"extension": "py",
"fi... | 2.734375 | stackv2 | # vim:ts=4 sw=4 expandtab softtabstop=4
from jsonmerge.exceptions import HeadInstanceError, \
BaseInstanceError, \
SchemaError
from jsonmerge.jsonvalue import JSONValue
import jsonschema
import re
class Strategy(object):
"""Base class for merge stra... | 295 | 34.87 | 118 | 20 | 2,335 | python | [] | 0 | true | |
2024-11-19T02:04:26.041045+00:00 | 1,583,916,895,000 | f9f4e9d1ee6bf2d0126a41cda93445a6aee2470c | 3 | {
"blob_id": "f9f4e9d1ee6bf2d0126a41cda93445a6aee2470c",
"branch_name": "refs/heads/master",
"committer_date": 1583916895000,
"content_id": "acd9cefdba8199cda6296f5989839b4c760aa693",
"detected_licenses": [
"MIT"
],
"directory_id": "cdb9983779cbc282fec4938e1a653ce0b0b028fc",
"extension": "py",
"fi... | 3.484375 | stackv2 | """
analyze.py
====================================
The Analysis Module for Moss
(For info. on how this works contact varshneybhupesh@gmail.com)
"""
class Node:
"""A Single Submitted file"""
def __init__(self, name):
self.name = name
self.tag = None
self.links = []
def pointTo(se... | 128 | 29.43 | 75 | 19 | 883 | python | [] | 0 | true | |
2024-11-19T02:04:26.093594+00:00 | 1,612,192,404,000 | 9afe3c49ecde3acbca55296ae3dabb79bcf5ee54 | 3 | {
"blob_id": "9afe3c49ecde3acbca55296ae3dabb79bcf5ee54",
"branch_name": "refs/heads/main",
"committer_date": 1612192404000,
"content_id": "1fc2322a531451e4672c195a28c20c822f41acfb",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "9d2106f30d3bb3907c11c4422dd67a75992bdba2",
"extension": "py",
... | 3.46875 | stackv2 | """
Returns a dataset of one-hot-encoded sequences of DNA and whether the motif of interest is present or absent in the data.
"""
import pandas as pd
import numpy as np
from sklearn.preprocessing import LabelEncoder, OneHotEncoder
from sklearn.model_selection import train_test_split
import joblib as jb
class MakeOH... | 114 | 28.75 | 121 | 16 | 814 | python | [] | 0 | true | |
2024-11-19T02:04:26.184627+00:00 | 1,498,021,142,000 | 274e7fa5130e5b74de30aad75fef2816fdb285ce | 3 | {
"blob_id": "274e7fa5130e5b74de30aad75fef2816fdb285ce",
"branch_name": "refs/heads/master",
"committer_date": 1498021142000,
"content_id": "f476f838a3954808029d1e5b82bacbcfb5bd944f",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "fdbc09cc5a33b83967c1d03284d03a837637945b",
"extension": "py"... | 3 | stackv2 | # -*- coding: utf-8 -*-
from __future__ import unicode_literals
from six import string_types, text_type, add_metaclass
from prettytable import PrettyTable
from neutronclient.v2_0 import client
from termcolor import cprint
from utils import format_tree, printo
import abc
import sys
import json
@add_metaclass(abc.ABCMe... | 144 | 28.28 | 73 | 16 | 1,001 | python | [] | 0 | true | |
2024-11-19T02:04:26.393969+00:00 | 1,531,366,899,000 | 994556178cc868c4fa432f51196554928a81e775 | 2 | {
"blob_id": "994556178cc868c4fa432f51196554928a81e775",
"branch_name": "refs/heads/master",
"committer_date": 1531366899000,
"content_id": "10df63b9fec2f703e49804f8910ea7fbcb5e371d",
"detected_licenses": [
"MIT"
],
"directory_id": "bd98f806de5a2beb819bf92d572395da2b34e897",
"extension": "py",
"fi... | 2.40625 | stackv2 | # References: https://pytorch.org/tutorials/intermediate/seq2seq_translation_tutorial.html
#
import torch
import torch.nn as nn
import torch.nn.functional as F
import torchvision.models as models
import numpy as np
import random
class EncoderCNN(nn.Module):
def __init__(self, embed_size, deep=False):
s... | 155 | 35.25 | 129 | 17 | 1,309 | python | [] | 0 | true | |
2024-11-19T02:04:26.499679+00:00 | 1,690,168,616,000 | f6392ae8d7bd69cdc88cdbee494b3254004bfae0 | 2 | {
"blob_id": "f6392ae8d7bd69cdc88cdbee494b3254004bfae0",
"branch_name": "refs/heads/master",
"committer_date": 1690168616000,
"content_id": "8d04184dd530a1856b35fa32fca4e82248b188ca",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "975df98f4897c0bbf7e508789c5b6ff10aad2647",
"extension": "py"... | 2.46875 | stackv2 | # Copyright (c) 2020, WSO2 Inc. (http://www.wso2.org) All Rights Reserved.
#
# WSO2 Inc. licenses this file to you 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/LICE... | 35 | 34.09 | 94 | 17 | 302 | python | [] | 0 | true | |
2024-11-19T02:04:26.956045+00:00 | 1,615,119,280,000 | 139f132a65fa38eb7887492f840da5dd51c12119 | 3 | {
"blob_id": "139f132a65fa38eb7887492f840da5dd51c12119",
"branch_name": "refs/heads/master",
"committer_date": 1615119280000,
"content_id": "238ee39ccec2d320a1148c8fca7cb5e7c38c4ed4",
"detected_licenses": [
"MIT"
],
"directory_id": "d8cda3656281d7a4b7ce8c1b8d1a92bc8ec362f0",
"extension": "py",
"fi... | 2.53125 | stackv2 | import numpy as np
from model import SkipGramModel
from torch.autograd import Variable
import torch
import torch.nn as nn
import torch.optim as optim
import sys
from data_handler import DataHanlder
class Word2Vec:
def __init__(self, log_filename: str,
output_filename: str,
embedd... | 87 | 41.83 | 114 | 19 | 847 | python | [] | 0 | true | |
2024-11-19T02:04:27.116547+00:00 | 1,618,062,731,000 | d1f68394a7a85cc2ad47bea5d55c6ee0d3d374ea | 4 | {
"blob_id": "d1f68394a7a85cc2ad47bea5d55c6ee0d3d374ea",
"branch_name": "refs/heads/main",
"committer_date": 1618062731000,
"content_id": "a8ae618a5ca0d0208d791077726e09a238564793",
"detected_licenses": [
"MIT"
],
"directory_id": "13d315c52759c6163c4d866d0d582b0751a0e7a2",
"extension": "py",
"file... | 3.6875 | stackv2 | from typing import List, Union
my_list: List[str] = ["first", "last", "something"]
if my_list:
result = " ".join(x for x in my_list)
print(result)
else:
print("The list is empty")
def show_name(name: str) -> str:
if not name:
return "Do not allowed empty name"
return name.upper()
x: Un... | 36 | 16.39 | 62 | 9 | 176 | python | [] | 0 | true | |
2024-11-19T02:04:27.233610+00:00 | 1,554,648,996,000 | b5722a016e88b49a48904e47904bca95aee51cb5 | 3 | {
"blob_id": "b5722a016e88b49a48904e47904bca95aee51cb5",
"branch_name": "refs/heads/master",
"committer_date": 1554648996000,
"content_id": "c4daddc0b1b7509fea840b1b6726a5a476f0a044",
"detected_licenses": [
"MIT"
],
"directory_id": "0889a6d48198f591b36a56ef2aed2e98ab951ae8",
"extension": "py",
"fi... | 3.25 | stackv2 | import socket
def Main():
host = '127.0.0.1'
port = 5000
#Create a communication port for the UDP protocolo using DATAGRAM
s = socket.socket(socket.AF_INET, socket.SOCK_DGRAM)
#Associating the address and the port to a process
s.bind((host, port))
print("Server started.")
while True... | 29 | 26.03 | 69 | 12 | 202 | python | [] | 0 | true | |
2024-11-19T02:04:27.352101+00:00 | 1,692,794,308,000 | 2ff095d3215182fcf39afb63e1785975ca60abaf | 2 | {
"blob_id": "2ff095d3215182fcf39afb63e1785975ca60abaf",
"branch_name": "refs/heads/main",
"committer_date": 1692794308000,
"content_id": "3f809e0e5dc682845d2712095bf1c6d95ecea6c5",
"detected_licenses": [
"BSD-2-Clause"
],
"directory_id": "bb78dac07e7f4afc2a57e10dc2047687fb917169",
"extension": "py"... | 2.359375 | stackv2 | # YouCompleteMe (https://github.com/ycm-core/YouCompleteMe) configuration file
# that enables it to find the LLVM and Python includes from a conda
# environment, independent of the Conda environment location and Python and
# LLVM versions.
import os
import sys
from pathlib import Path
CONDA_PREFIX = os.environ['COND... | 40 | 27.93 | 78 | 9 | 296 | python | [] | 0 | true | |
2024-11-19T02:04:27.459391+00:00 | 1,595,355,098,000 | 6b2d4b6878ff9d55c6575e3e01b9a5bcce0c1a15 | 3 | {
"blob_id": "6b2d4b6878ff9d55c6575e3e01b9a5bcce0c1a15",
"branch_name": "refs/heads/master",
"committer_date": 1595355098000,
"content_id": "e5479e5f9890a2456cae02f99de32ad0412b9c7a",
"detected_licenses": [
"MIT"
],
"directory_id": "8367320f201518a8cf74261110b0db91519e155e",
"extension": "py",
"fi... | 2.953125 | stackv2 | from datetime import timezone
import pandas as pd
def get_utc_naive(dt):
"""Convert timezone aware timestamp to UTC naive timestamp."""
return dt.astimezone(timezone.utc).replace(tzinfo=None)
def parse_timestamp_str(time):
"""Get naive datetime in UTC."""
# manual version
# datetime.strptime(ti... | 17 | 31.12 | 83 | 10 | 133 | python | [] | 0 | true | |
2024-11-19T02:04:28.277829+00:00 | 1,619,792,770,000 | 98f8e1202637330001070418826e77409c6d4683 | 3 | {
"blob_id": "98f8e1202637330001070418826e77409c6d4683",
"branch_name": "refs/heads/master",
"committer_date": 1619792770000,
"content_id": "39a2efaa8cb79dd22fec3050418a2e41f302754f",
"detected_licenses": [
"MIT"
],
"directory_id": "0747c5f48b4681bfbb0caddac0adcafaa584bc25",
"extension": "py",
"fi... | 2.609375 | stackv2 | #!/usr/bin/env python3
from typing import Dict, Optional
import qcelemental as qcel
import qcengine as qcng
from pydantic import Field, PositiveInt, validator
from qubekit.utils.datastructures import SchemaBase
from qubekit.utils.exceptions import SpecificationError
class BaseEngine(SchemaBase):
"""
A comm... | 75 | 29.53 | 110 | 14 | 522 | python | [] | 0 | true | |
2024-11-19T02:04:28.332789+00:00 | 1,505,157,942,000 | f67ceb11dd187b85a480ebbdee5c1c6ea9007273 | 4 | {
"blob_id": "f67ceb11dd187b85a480ebbdee5c1c6ea9007273",
"branch_name": "refs/heads/master",
"committer_date": 1505157942000,
"content_id": "d94f5463494f6d95d38e85fefa7235d0fea8fb87",
"detected_licenses": [
"MIT-feh"
],
"directory_id": "2e46ef9411a8e84074d35338240a69f65b7a6a5d",
"extension": "py",
... | 3.5 | stackv2 | import matplotlib.pyplot as plt
def plotFile(filename, plot_title):
xpoints = []
ypoints = []
ypointsExpected = []
ypointsErrorPlus = []
ypointsErrorMinus = []
with open(filename, 'r') as f:
f.readline() # skip the first line
expected = float(f.readline().split()[1])
... | 34 | 30.35 | 91 | 15 | 274 | python | [] | 0 | true | |
2024-11-19T02:04:28.381006+00:00 | 1,586,446,517,000 | de71f5922bb1fe77237d72d2d91ab7dd17b1fe92 | 3 | {
"blob_id": "de71f5922bb1fe77237d72d2d91ab7dd17b1fe92",
"branch_name": "refs/heads/master",
"committer_date": 1586446517000,
"content_id": "3416016178a0b156b58314b0e84b3a506c9b9a79",
"detected_licenses": [
"MIT"
],
"directory_id": "2df02a50773f87d2d507cadf4ecad10d7a176c0b",
"extension": "py",
"fi... | 3.3125 | stackv2 | from pymongo import MongoClient
class Database:
"""
The class for the Database.
"""
def __init__(self):
"""Initialize the Database."""
self.client = MongoClient()
self.db = self.client.highscores
def insert(self, name, score):
""""Insert a record into the database."... | 40 | 26.25 | 88 | 15 | 254 | python | [] | 0 | true | |
2024-11-19T02:04:28.444494+00:00 | 1,613,926,231,000 | 644d479fa7837e8964c802c1548bb47e6096880c | 4 | {
"blob_id": "644d479fa7837e8964c802c1548bb47e6096880c",
"branch_name": "refs/heads/master",
"committer_date": 1613926231000,
"content_id": "f0a8a8e1fe27a65b0ac5c3422c6b38fd7d091a70",
"detected_licenses": [
"MIT"
],
"directory_id": "7366f292ede1ce81481e4b07fab70433a854012f",
"extension": "py",
"fi... | 3.6875 | stackv2 | """
230. Kth Smallest Element in a BST
"""
# Definition for a binary tree node.
class TreeNode:
def __init__(self, val=0, left=None, right=None):
self.val = val
self.left = left
self.right = right
class Solution:
def kthSmallest(self, root: TreeNode, k: int) -> int:
A = []
... | 30 | 22.8 | 57 | 14 | 170 | python | [] | 0 | true | |
2024-11-19T02:04:28.539591+00:00 | 1,691,093,966,000 | 342c93c1032ca64df157907f9ee040f0072759c7 | 3 | {
"blob_id": "342c93c1032ca64df157907f9ee040f0072759c7",
"branch_name": "refs/heads/master",
"committer_date": 1691093966000,
"content_id": "2d6740a8d36ddc07f7ff459709b4761460af4e57",
"detected_licenses": [
"MIT"
],
"directory_id": "7b97691e0493885e85cce9cc6f0f19ee54024a1d",
"extension": "py",
"fi... | 2.53125 | stackv2 | # -*- coding: utf-8 -*-
"""
EPA REI
"""
import re
import pandas as pd
import numpy as np
from flowsa.location import US_FIPS
from flowsa.flowbyfunctions import assign_fips_location_system
from flowsa.fbs_allocation import direct_allocation_method
def rei_url_helper(*, build_url, config, **_):
"""
This helper ... | 206 | 37.1 | 88 | 24 | 1,932 | python | [] | 0 | true | |
2024-11-19T02:04:28.906183+00:00 | 1,567,875,768,000 | 5e3f9c57e95b287501bc111936081a6bfcc55b3e | 3 | {
"blob_id": "5e3f9c57e95b287501bc111936081a6bfcc55b3e",
"branch_name": "refs/heads/master",
"committer_date": 1567875768000,
"content_id": "2f1bee32e8899b5f6f16b271a5fefb08dca449df",
"detected_licenses": [
"MIT"
],
"directory_id": "b072a98e605a8325cf79efec92ffc564bd588916",
"extension": "py",
"fi... | 2.515625 | stackv2 | '''
example for extracting faces
extract all faces from images from path - directory (with all subdirectories)
upload_path - directory, where faces will be stored
'''
import sys, os
sys.path.append(os.path.join(os.path.dirname(__file__), '..'))
import facextr
path = r'\\123tb\Media\Photo\Pictures\2019'
upload_path... | 20 | 22.45 | 77 | 10 | 125 | python | [] | 0 | true | |
2024-11-19T02:04:29.007477+00:00 | 1,534,813,260,000 | dc77b0cd0fd0a0fa0280efb26927b7aafe8308d1 | 3 | {
"blob_id": "dc77b0cd0fd0a0fa0280efb26927b7aafe8308d1",
"branch_name": "refs/heads/master",
"committer_date": 1534813260000,
"content_id": "acd31c02f17d1b85e837a202584efcd7273867ac",
"detected_licenses": [],
"directory_id": "ae0da6810a81a442ad1dc737071d3da5a926444c",
"extension": "py",
"filename": "dem... | 2.984375 | stackv2 | from kernel_exp_family.estimators.finite.gaussian import KernelExpFiniteGaussian
from kernel_exp_family.examples.demo_simple import ground_truth
from kernel_exp_family.examples.tools import pdf_grid, visualise_array
import matplotlib.pyplot as plt
import numpy as np
if __name__ == '__main__':
"""
Simple examp... | 73 | 28.1 | 80 | 13 | 581 | python | [] | 0 | true | |
2024-11-19T02:04:29.129440+00:00 | 1,491,765,360,000 | 2837cd0768ab896f4ede14912110fe1f95da023e | 3 | {
"blob_id": "2837cd0768ab896f4ede14912110fe1f95da023e",
"branch_name": "refs/heads/master",
"committer_date": 1491765360000,
"content_id": "429075b18c26fafc1c078460c2ba17a659e40feb",
"detected_licenses": [
"MIT"
],
"directory_id": "4afc9596fc050d86be513f2ce8f13ac0203c6df0",
"extension": "py",
"fi... | 3.046875 | stackv2 | import random
import datetime
import discord
from asteval import Interpreter
from discord.ext import commands
from dog import Cog
from dog.util import pretty_timedelta, make_profile_embed, american_datetime
class Utility(Cog):
@commands.command()
async def avatar(self, ctx, target: discord.User=None):
... | 87 | 32.32 | 85 | 18 | 584 | python | [] | 0 | true | |
2024-11-19T02:04:29.472429+00:00 | 1,582,193,433,000 | e8000f7b5c442376c3d838d3625b705e6d0b2575 | 2 | {
"blob_id": "e8000f7b5c442376c3d838d3625b705e6d0b2575",
"branch_name": "refs/heads/master",
"committer_date": 1582193433000,
"content_id": "1cbebb0ca1313739b0fe47f6d54aaa9f17675ecf",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "0806f688477a0bdc229930cc3457f59b9b5c029f",
"extension": "py"... | 2.4375 | stackv2 | import base64
import json
import jwt
import requests
from django.conf import settings
from django.contrib.auth import get_user_model
from django.contrib.auth.backends import ModelBackend
USER_MODEL = get_user_model()
class ALBAuth(ModelBackend):
def authenticate(self, request, **kwargs):
if request:
... | 64 | 29.45 | 75 | 14 | 442 | python | [] | 0 | true | |
2024-11-19T02:04:29.798132+00:00 | 1,610,491,514,000 | aa55fa9faec2a2b3f373f6cc6ea03a68c2fd8e04 | 3 | {
"blob_id": "aa55fa9faec2a2b3f373f6cc6ea03a68c2fd8e04",
"branch_name": "refs/heads/master",
"committer_date": 1610491514000,
"content_id": "62baaf5b81ad0b9e5c1c3d24505b500eb9f65ff0",
"detected_licenses": [
"MIT"
],
"directory_id": "834f1ea1e83de05bfc0fc70d2a6ab2adb21a7858",
"extension": "py",
"fi... | 2.59375 | stackv2 | # -*- coding: utf-8 -*-
import numpy as np
from tqdm import tqdm
import os
import re
import nltk
# nltk.download('punkt', 'stopwords')
from nltk.corpus import stopwords
from nltk.tokenize import word_tokenize
from nltk.tokenize import TweetTokenizer
tweet_tokenizer = TweetTokenizer()
from nltk.stem.lancaster import L... | 168 | 38.74 | 137 | 20 | 1,582 | python | [] | 0 | true | |
2024-11-19T02:04:29.994840+00:00 | 1,499,844,634,000 | dbfb225895a6bcf9990def7b63fd39b7b07b02e1 | 3 | {
"blob_id": "dbfb225895a6bcf9990def7b63fd39b7b07b02e1",
"branch_name": "refs/heads/master",
"committer_date": 1499844634000,
"content_id": "09fd489538c01dfbe294f5ff0a0a1f55275430d4",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "53c275639e60f68ec2805f162c022fa5442207c0",
"extension": "py"... | 2.625 | stackv2 | from data import *
if __name__ == "__main__":
df, stats_df = create_training_set("data/fb.csv")
'''
for i in range(len(df.columns)-1):
for j in range(i+1,len(df.columns)-1):
x = df.columns[i]
y = df.columns[j]
plotModel = PlotModel(title="Log Reg", xlabel=x, ylabel=y)
plot_logistic_regression_data(df... | 99 | 31.93 | 90 | 10 | 1,079 | python | [] | 0 | true | |
2024-11-19T02:04:40.736762+00:00 | 1,571,567,087,000 | 550a81009c37b50a71f30978c8b0fdcd08da3a97 | 3 | {
"blob_id": "550a81009c37b50a71f30978c8b0fdcd08da3a97",
"branch_name": "refs/heads/master",
"committer_date": 1571567087000,
"content_id": "4fd43051fc1ecd5b1f65ced67a93d4c6bde5593f",
"detected_licenses": [
"MIT"
],
"directory_id": "8581a7f6537f37e5446833248bde354ab8d04eee",
"extension": "py",
"fi... | 3.03125 | stackv2 |
from fbprophet import Prophet
import requests
import pandas as pd
from datetime import datetime
def find(num):
i = int(num**(0.5))
while(num%i != 0):
i -= 1
return i,int(num/i)
def find(num):
i = int(num**(0.5))
while(num%i != 0):
i -= 1
return i,int(num/i)
def mlfun (co... | 51 | 29.73 | 130 | 15 | 494 | python | [] | 0 | true | |
2024-11-19T02:04:40.880322+00:00 | 1,536,247,035,000 | c871a66ff9da43ce314d1d4bdea95dfad4cc0caa | 3 | {
"blob_id": "c871a66ff9da43ce314d1d4bdea95dfad4cc0caa",
"branch_name": "refs/heads/master",
"committer_date": 1536247035000,
"content_id": "c7456dbbc39a8d06a41c593f65f400ae892c235f",
"detected_licenses": [
"MIT"
],
"directory_id": "56f1416a81ded9e6c31fe87ac771453141c1a216",
"extension": "py",
"fi... | 2.828125 | stackv2 | import read_file
from random import shuffle
import math
import pandas as pd
import copy
import timeout_decorator
people_org, heavy_shifts_org, night_shifts_org, other_shifts_org = read_file.get_info()
avg_hours = 33
@timeout_decorator.timeout(2)
def allocate():
people = copy.deepcopy(people_org)
heavy_shifts... | 93 | 30.09 | 143 | 14 | 687 | python | [] | 0 | true | |
2024-11-19T02:04:40.929742+00:00 | 1,683,555,061,000 | 1092e170aad9759d90e40a41ddacfd17368c98ab | 3 | {
"blob_id": "1092e170aad9759d90e40a41ddacfd17368c98ab",
"branch_name": "refs/heads/main",
"committer_date": 1683555061000,
"content_id": "331c52ad8b3ce38ad9ef9428e5b628d4d70b699b",
"detected_licenses": [
"MIT"
],
"directory_id": "7a5c7352e5b5452a95f09d2b99a032d9e72738f4",
"extension": "py",
"file... | 3.125 | stackv2 | import numpy as np
from numpy import ndarray
def ravel_hash_func(data: ndarray, dimensions: ndarray) -> ndarray:
assert data.shape[1] == dimensions.shape[0]
hash_values = data[:, 0].copy()
for i in range(1, dimensions.shape[0]):
hash_values *= dimensions[i]
hash_values += data[:, i]
re... | 80 | 41.38 | 117 | 15 | 782 | python | [] | 0 | true | |
2024-11-19T02:04:41.265299+00:00 | 1,573,405,324,000 | 6277f8ba4c1637d446714c29bd7860ec51b2f59a | 3 | {
"blob_id": "6277f8ba4c1637d446714c29bd7860ec51b2f59a",
"branch_name": "refs/heads/master",
"committer_date": 1573405460000,
"content_id": "f015de089985d37f1558cd19beccb8b0623dbe56",
"detected_licenses": [
"MIT"
],
"directory_id": "bfc67c3a3b70043e3ea0dc3a7c4d38133bdbb843",
"extension": "py",
"fi... | 2.90625 | stackv2 | #!/usr/bin/env python3
import cv2
import glob
import numpy as np
class Model:
def calibrate(self, nx, ny):
images = list()
image_paths = glob.glob("camera_cal/*.jpg")
print("Loading calibration images ...")
for path in image_paths:
images.append(cv2.imread(path))
... | 58 | 30.47 | 73 | 13 | 415 | python | [] | 0 | true | |
2024-11-19T02:04:41.381646+00:00 | 1,389,212,414,000 | 5b08e640518236cb903209bd33cb48f11fa48bcb | 3 | {
"blob_id": "5b08e640518236cb903209bd33cb48f11fa48bcb",
"branch_name": "refs/heads/master",
"committer_date": 1389212414000,
"content_id": "26e32cb094c93d8d1f4f262d02ecaa2ab6954b53",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "9f3175348f460560548c56ecfadfff00f1890a9f",
"extension": "py"... | 2.546875 | stackv2 | #
# Copyright 2013 Xavier Bruhiere
#
# 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 in wri... | 79 | 36.47 | 79 | 22 | 613 | python | [] | 0 | true | |
2024-11-19T02:04:41.510255+00:00 | 1,675,317,880,000 | 3a72f0d06b0877817278e11802f25744efd39cc5 | 3 | {
"blob_id": "3a72f0d06b0877817278e11802f25744efd39cc5",
"branch_name": "refs/heads/master",
"committer_date": 1675317880000,
"content_id": "5531208acd60aeccff5b46341fa72eb7d6e8ea70",
"detected_licenses": [
"MIT"
],
"directory_id": "fcd29745ed7a66b46f5039c2ad07f2fa5cb457a2",
"extension": "py",
"fi... | 3.390625 | stackv2 | import re
import logging
from locators.book_locators import BookLocators
logger = logging.getLogger('scraping.book_parser')
class BookParser:
"""
A class to take in an HTML page or content, and find properties of an item
in it.
"""
RATINGS = {
'One': 1,
'Two': 2,
'Three'... | 72 | 29.69 | 78 | 14 | 521 | python | [] | 0 | true | |
2024-11-19T02:04:41.641593+00:00 | 1,625,766,041,000 | 1bd6573324e513b2aceee7da92ee9662a056804a | 2 | {
"blob_id": "1bd6573324e513b2aceee7da92ee9662a056804a",
"branch_name": "refs/heads/main",
"committer_date": 1625766041000,
"content_id": "c933893ce7da4d6c0357312c586558890931865f",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "1c5b0d9086a4006a7ddd88a7c3de6be331f037c6",
"extension": "py"... | 2.484375 | stackv2 | #!/usr/bin/python3
import rospy
import time
import math
import threading
from sensor_msgs.msg import LaserScan
from std_msgs.msg import String
import numpy as np
from autonomous_traversal.srv import ClearService, ClearServiceResponse
class SickAvoider:
def __init__(self):
self.np_ranges = None
s... | 76 | 38.46 | 95 | 18 | 845 | python | [] | 0 | true | |
2024-11-19T02:04:41.699572+00:00 | 1,684,690,217,000 | 5e24fb1d03bf3882896f00b166aecf8532d3489e | 3 | {
"blob_id": "5e24fb1d03bf3882896f00b166aecf8532d3489e",
"branch_name": "refs/heads/master",
"committer_date": 1684690217000,
"content_id": "f2c5a3c8c77f0b8b926cd330642aa8cc30724f37",
"detected_licenses": [
"MIT"
],
"directory_id": "434cc3982d25f1d83b9f0a7ec9c72b588652bc1d",
"extension": "py",
"fi... | 3.46875 | stackv2 | import turtle
from mpmath import mp # Für die Ziffern von Pi
pieter = turtle.Turtle()
mp.dps = 1000
pi = (f"{(mp.pi)}")
digits = [int(s) for s in pi if s != "."]
for m in digits:
if m == 0:
pieter.forward(10)
if m == 1:
pieter.right(90)
pieter.forward(10)
if m == 2:
pie... | 40 | 19.73 | 49 | 9 | 291 | python | [] | 0 | true | |
2024-11-19T02:04:41.766353+00:00 | 1,619,259,210,000 | b36a02b089a3a959748e2fdc452418170a9d64b2 | 3 | {
"blob_id": "b36a02b089a3a959748e2fdc452418170a9d64b2",
"branch_name": "refs/heads/master",
"committer_date": 1619259210000,
"content_id": "e5a103fd825fdf88e6f4dc7846a390ab14376072",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "544e0c8d5dee137a4ef133a53267c6eca489015b",
"extension": "p... | 3 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on May 31 2019
@author: Nathan de Lara <ndelara@enst.fr>
@author: Thomas Bonald <bonald@enst.fr>
"""
from typing import Union, Optional
import numpy as np
from scipy import sparse
from scipy.sparse.linalg import LinearOperator
from sknetwork.linalg.ppr_solver... | 176 | 34.96 | 119 | 15 | 1,558 | python | [] | 0 | true | |
2024-11-19T02:04:41.821283+00:00 | 1,631,906,914,000 | 036dc8b8f084fe63a3dd14c18786f19f401ecce7 | 3 | {
"blob_id": "036dc8b8f084fe63a3dd14c18786f19f401ecce7",
"branch_name": "refs/heads/main",
"committer_date": 1631906914000,
"content_id": "5554e5398ee621f8d1e39b5c932e2599cfe9888f",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "60e0360d02af05ea15b21ae29ba6c3d4b1cf113b",
"extension": "py"... | 2.546875 | stackv2 | #!/usr/bin/env python3
# -*- coding: utf-8 -*-
#
# cylinder.py
# ffsas: free-form inversion for small-angle scattering
# Copyright © 2021 SciML, STFC, UK. All rights reserved.
""" cylinder model class """
import math
import torch
from scipy.special import j1
from ffsas.models.base import SASModel
class Cylinder(... | 77 | 30.35 | 80 | 18 | 724 | python | [] | 0 | true | |
2024-11-19T02:04:41.877721+00:00 | 1,581,657,781,000 | b1ac079699673bf66ff10f7f74bb3ec8d607d792 | 2 | {
"blob_id": "b1ac079699673bf66ff10f7f74bb3ec8d607d792",
"branch_name": "refs/heads/master",
"committer_date": 1581657781000,
"content_id": "07ed9b1065a658a75fb4cbb5222c4f122ac6012e",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "6b791247919f7de90c8402abcca64b32edd7a29b",
"extension": "py"... | 2.46875 | stackv2 | # uncompyle6 version 3.2.4
# Python bytecode 2.7 (62211)
# Decompiled from: Python 2.7.15 (v2.7.15:ca079a3ea3, Apr 30 2018, 16:30:26) [MSC v.1500 64 bit (AMD64)]
# Embedded file name: direct.showutil.Rope
from panda3d.core import *
import types
class Rope(NodePath):
showRope = ConfigVariableBool('show-rope', True,... | 88 | 36.2 | 109 | 19 | 835 | python | [] | 0 | true | |
2024-11-19T02:04:41.927281+00:00 | 1,618,673,596,000 | 8a1721516d8bfe9f321a203dd54a3549d4ff0bbf | 3 | {
"blob_id": "8a1721516d8bfe9f321a203dd54a3549d4ff0bbf",
"branch_name": "refs/heads/main",
"committer_date": 1618673596000,
"content_id": "94a0c0fb5bcb605eb36778f511099dc3643b6add",
"detected_licenses": [
"MIT"
],
"directory_id": "41ac9e7877350b8da446c670d781f08ccc1b1d48",
"extension": "py",
"file... | 2.515625 | stackv2 | """
An RFC8771-compliant implementation of the Internationalized Deliberately Unreadable Network Notation (shortened as I-DUNNO)
"""
import collections
import functools
import ipaddress
import itertools
import random
from . import data
__all__ = ['encode', 'decode']
__version__ = '0.1.3'
utf8_lengths = [(0, 7)... | 171 | 31.19 | 144 | 15 | 1,419 | python | [] | 0 | true | |
2024-11-19T02:04:42.028090+00:00 | 1,629,239,727,000 | 450f77fe766187244556e046e1d12e3f8557a364 | 3 | {
"blob_id": "450f77fe766187244556e046e1d12e3f8557a364",
"branch_name": "refs/heads/master",
"committer_date": 1629239727000,
"content_id": "cb54e653a02f6c97c7bef15366e714d20d68e384",
"detected_licenses": [
"MIT"
],
"directory_id": "d594e26a17a8bea079b2dbeba764cfc95cc786c7",
"extension": "py",
"fi... | 3.125 | stackv2 | # -*- coding: utf-8 -*-
####################################################################################################
# File: path_finding_utils.py
# Purpose: Path finding utility functions used in the various algorithms. Includes the main cost
# function used as the metric on the graphs.
#
# Auth... | 341 | 32.53 | 157 | 18 | 3,134 | python | [] | 0 | true | |
2024-11-19T02:04:42.139323+00:00 | 1,532,437,961,000 | 93385cf4eab54715ab5866d11fb986067272f75c | 3 | {
"blob_id": "93385cf4eab54715ab5866d11fb986067272f75c",
"branch_name": "refs/heads/master",
"committer_date": 1532437961000,
"content_id": "27240dd15beedc727cbdcf4dea0fe160959a0325",
"detected_licenses": [
"MIT"
],
"directory_id": "8923fe8a8a7ff6bdafacb2af6c4439bbe0ad0559",
"extension": "py",
"fi... | 2.8125 | stackv2 | import gzip
import os
import re
import xmltodict
from lxml import etree
import constants as c
def get_docs_list_by_subdir(wikipedia_dir):
"""
read into the dataset_dir and yield subdirs and files.
:param wikipedia_dir: the path to the dataset directory
:return: a tuple (current_subdir, list_of_filepaths), wher... | 185 | 28.01 | 105 | 19 | 1,515 | python | [] | 0 | true | |
2024-11-19T02:04:42.188925+00:00 | 1,658,770,485,000 | 83cca729ddb3e8f3e349c13c140044c2f5c3adea | 2 | {
"blob_id": "83cca729ddb3e8f3e349c13c140044c2f5c3adea",
"branch_name": "refs/heads/master",
"committer_date": 1658770485000,
"content_id": "377b5597a95885a5a7b185498dc28d5b2734e8b8",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "344308d52715284af5d401116b10e77fce4172b3",
"extension": "p... | 2.34375 | stackv2 | from dataclasses import dataclass
from enum import Enum
from typing import List, Dict, Union
from loguru import logger
from dome9 import Dome9Resource, Client, BaseDataclassRequest
from dome9.consts import CloudVendors
from dome9.exceptions import UnsupportedRuleEntitySeverity, UnsupportedCloudVendor
class RuleEnti... | 133 | 26.1 | 117 | 15 | 957 | python | [] | 0 | true | |
2024-11-19T02:04:42.247058+00:00 | 1,607,362,745,000 | 56b7529383076fe42c70539c617881412fac29f5 | 2 | {
"blob_id": "56b7529383076fe42c70539c617881412fac29f5",
"branch_name": "refs/heads/master",
"committer_date": 1607362745000,
"content_id": "32f694cbe00f7d539d16cafe0f85fe655fe1109e",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "16a2ac198a36d7633c62d41f4604356cd0ae732e",
"extension": "py"... | 2.484375 | stackv2 | #!/usr/bin/python
import argparse, sys, subprocess, re, os
import GenePredBasics
from shutil import rmtree
from random import randint
# Pre: Take a sorted BAM file and find novel regions at various depths.
# It also can take read annotations from annotate_psl_with_gpd.py
# But if no read annotation is given,... | 162 | 37.4 | 178 | 29 | 1,695 | python | [] | 0 | true | |
2024-11-19T02:04:42.446225+00:00 | 1,527,155,846,000 | bf14a7d3788abf9edb6731d3d3977a0cdb49cb66 | 3 | {
"blob_id": "bf14a7d3788abf9edb6731d3d3977a0cdb49cb66",
"branch_name": "refs/heads/master",
"committer_date": 1527155846000,
"content_id": "a2ef33375579295818da5ab6efe0a62d4e9a8cd1",
"detected_licenses": [
"MIT"
],
"directory_id": "cc100e9362222e140e3088e1d2dcc38872d811fd",
"extension": "py",
"fi... | 2.625 | stackv2 | import pymysql
import pymysql.cursors
from config.config import *
# API Reference: http://pymysql.readthedocs.io/en/latest/modules/
class SqlHelper:
'''mysql helper'''
def __init__(self):
self.connection = None
self.connect()
def connect(self):
if not self.connection:
self.connection = pymysql.connect(
... | 78 | 21.86 | 65 | 15 | 454 | python | [] | 0 | true | |
2024-11-19T02:04:42.613152+00:00 | 1,534,083,540,000 | ee29cf0e354f63a6a20deba978905719febe2ebe | 3 | {
"blob_id": "ee29cf0e354f63a6a20deba978905719febe2ebe",
"branch_name": "refs/heads/master",
"committer_date": 1534083540000,
"content_id": "e8363e86512ebf18a4978b99fca86cbbc6b0f678",
"detected_licenses": [
"MIT"
],
"directory_id": "769664f52bc154557537f6f42e616fa9b4fcf8d9",
"extension": "py",
"fi... | 2.828125 | stackv2 | from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.utils.validation import check_X_y, check_array, check_is_fitted
from sklearn.utils.multiclass import unique_labels
from sklearn.metrics import euclidean_distances
import numpy as np
class TemplateClassifier(BaseEstimator, ClassifierMixin):
def ... | 85 | 31.47 | 100 | 17 | 623 | python | [] | 0 | true | |
2024-11-19T02:04:42.672088+00:00 | 1,692,365,184,000 | b68fecbcec91982b71dbc45588fc141f234932df | 2 | {
"blob_id": "b68fecbcec91982b71dbc45588fc141f234932df",
"branch_name": "refs/heads/master",
"committer_date": 1692365184000,
"content_id": "fd67725d6ff923506dbc99844a844b3d54f7c3ae",
"detected_licenses": [
"MIT"
],
"directory_id": "5809048b0ef54940da88b4fcabcca7784d09cb87",
"extension": "py",
"fi... | 2.34375 | stackv2 | #!/usr/bin/python
#
# [ DEPRECATED ]
# Use md2conf.py which has been written using the REST API instead of XMLRPC
#
#
# @rmoff 20140330
#
# "A Hack But It Works"
#----------------------------------------
#
# This will import specified Markdown file into Confluence
#
# It uses [depreciated] XMLRPC API for Confluence. S... | 140 | 40.14 | 167 | 22 | 1,592 | python | [] | 0 | true | |
2024-11-19T02:04:42.765361+00:00 | 1,669,618,498,000 | 694770acda05221aa0db63e65c31acf112fe924f | 2 | {
"blob_id": "694770acda05221aa0db63e65c31acf112fe924f",
"branch_name": "refs/heads/master",
"committer_date": 1669618498000,
"content_id": "7d227a4c39bd2e8352378a8244d2dff354ff8171",
"detected_licenses": [
"MIT"
],
"directory_id": "d1adff95d1e1685cac220db38c770a4036b6e4a4",
"extension": "py",
"fi... | 2.3125 | stackv2 | """
Michael duPont - michael@mdupont.com
config.py - Shared METAR display settings
"""
import json
import logging
from os import path
from pathlib import Path
# Seconds between server pings
update_interval = 600
# Seconds between connection retries
timeout_interval = 60
# Set log level - CRITICAL, ERROR, WARNING, I... | 61 | 21.61 | 63 | 10 | 365 | python | [] | 0 | true | |
2024-11-19T02:04:43.020821+00:00 | 1,625,680,501,000 | 5c40098283d430757ab3e73623e533451bc8b04e | 3 | {
"blob_id": "5c40098283d430757ab3e73623e533451bc8b04e",
"branch_name": "refs/heads/main",
"committer_date": 1625680501000,
"content_id": "c4c5dd8b285f3906f1e39175b8df0f1e46bb2032",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "72b4e39803a129fc9a84d4422f55bea7ddc7068a",
"extension": "py",
... | 2.828125 | stackv2 | import ujson as json
from util.line_corpus import jsonl_lines
import argparse
parser = argparse.ArgumentParser()
parser.add_argument("--predictions_a", default=None, type=str, required=True)
parser.add_argument("--predictions_b", default=None, type=str, required=True)
args = parser.parse_args()
def qid2predictions(... | 36 | 30.64 | 84 | 15 | 342 | python | [] | 0 | true | |
2024-11-19T02:04:43.154451+00:00 | 1,602,419,808,000 | 1b1ab3c557a3dcecf2cfc9b5840ce381387df762 | 4 | {
"blob_id": "1b1ab3c557a3dcecf2cfc9b5840ce381387df762",
"branch_name": "refs/heads/master",
"committer_date": 1602419808000,
"content_id": "d838316b4f1e6d6505ec5c5e30824ab2e284f8a6",
"detected_licenses": [
"MIT"
],
"directory_id": "c364fca8ae4c896dee2c8b0dc545f4d73c8c8314",
"extension": "py",
"fi... | 3.5 | stackv2 | #!/usr/bin/env python3
"""
Write a function that performs a random crop of an image:
image is a 3D tf.Tensor containing the image to crop
size is a tuple containing the size of the crop
Returns the cropped image
"""
import tensorflow as tf
import numpy as np
def crop_image(image, size):
"""Crop image"""
hei... | 22 | 27.64 | 60 | 9 | 144 | python | [] | 0 | true | |
2024-11-19T02:04:43.274883+00:00 | 1,635,450,788,000 | 4e5d20cb8ad2ef9657365d342aa4ae74d1595616 | 3 | {
"blob_id": "4e5d20cb8ad2ef9657365d342aa4ae74d1595616",
"branch_name": "refs/heads/master",
"committer_date": 1635450788000,
"content_id": "18122a660b71c7f52adc3c1947e341483a2fd6ac",
"detected_licenses": [
"MIT"
],
"directory_id": "d4c930fb2732b8b658e624acfdc4967e9d41db20",
"extension": "py",
"fi... | 2.9375 | stackv2 | """
domonic.webapi.permissions
====================================
https://developer.mozilla.org/en-US/docs/Web/API/Permissions
"""
class PermissionStatus:
"""
The PermissionStatus interface represents the current state of a permission.
"""
def __init__(self, status):
self.status =... | 70 | 19.16 | 80 | 10 | 289 | python | [] | 0 | true | |
2024-11-19T02:04:43.348608+00:00 | 1,590,640,815,000 | fa21462c13c3ba79f704ecc22d49ed300a49bf4f | 3 | {
"blob_id": "fa21462c13c3ba79f704ecc22d49ed300a49bf4f",
"branch_name": "refs/heads/master",
"committer_date": 1590640815000,
"content_id": "a1d998d75e00a0bf119d6797a39cc090c2b78e1b",
"detected_licenses": [
"MIT"
],
"directory_id": "52d186ab5ac3381442043f9602e8c360ce018a9a",
"extension": "py",
"fi... | 2.625 | stackv2 | #!/usr/bin/python3
# -*- coding:utf-8 -*-
from tensorflow import keras
from MultiHeadAttention import MultiHeadAttention
from FFN import feed_forward_network
# Encoder Layer
class EncoderLayer(keras.layers.Layer):
"""
x -> self attention -> add & normalize & dropout -> feed_forward -> add & normalize & dropou... | 36 | 40.28 | 97 | 11 | 376 | python | [] | 0 | true | |
2024-11-19T02:04:43.406560+00:00 | 1,549,573,934,000 | 9634046727e376a63f63bc0ff38d491b9017a366 | 3 | {
"blob_id": "9634046727e376a63f63bc0ff38d491b9017a366",
"branch_name": "refs/heads/master",
"committer_date": 1549573934000,
"content_id": "36b724c1a18cb6a74c83b9b1b178583355b7112c",
"detected_licenses": [
"MIT"
],
"directory_id": "58b74e3dcf7c67de2ce84b6b408c2a9b0484d67b",
"extension": "py",
"fi... | 2.546875 | stackv2 | from flask import Flask
from flask import request
import requests
import json
app = Flask(__name__)
@app.route("/")
def index():
response = requests.get('https://api.etsy.com/v2/listings/active?api_key=cdwxq4soa7q4zuavbtynj8wx&keywords=bicycle&includes=Images,Shop&sort_on=score')
data = response.json()
... | 40 | 17.57 | 155 | 12 | 190 | python | [] | 0 | true | |
2024-11-19T02:04:43.586918+00:00 | 1,614,395,396,000 | 16e2bf29fb9222f92244aacca5a5818ed3ea1a52 | 3 | {
"blob_id": "16e2bf29fb9222f92244aacca5a5818ed3ea1a52",
"branch_name": "refs/heads/main",
"committer_date": 1614395396000,
"content_id": "a98381961bf547a9ee39c01aca9f005a5d39fdf4",
"detected_licenses": [
"MIT"
],
"directory_id": "a3196b27e2847968aabe367ff3ea869d13cb5bb1",
"extension": "py",
"file... | 2.546875 | stackv2 | import json
from dataclasses import dataclass
from functools import cached_property
from typing import (Any, ClassVar, Dict, List, Literal, Optional, Sequence,
Union)
from eth_utils import to_checksum_address
from hexbytes import HexBytes
from web3 import Web3
class EtherscanClientException(Excep... | 108 | 38.06 | 142 | 15 | 1,038 | python | [] | 0 | true | |
2024-11-19T02:04:43.851935+00:00 | 1,355,772,926,000 | 64917b3158788fe931f078fdc4d74cf0f5e64256 | 2 | {
"blob_id": "64917b3158788fe931f078fdc4d74cf0f5e64256",
"branch_name": "refs/heads/master",
"committer_date": 1355772926000,
"content_id": "ac6577857625b086c72cdf364b0554afa92cc21b",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "334e89507cd561ae90cadffd396917cb47c3e4cc",
"extension": "p... | 2.4375 | stackv2 | __author__ = 'Dr. Masroor Ehsan'
__email__ = 'masroore@gmail.com'
__copyright__ = 'Copyright 2013, Dr. Masroor Ehsan'
__license__ = 'BSD'
__version__ = '0.1.1'
try:
import simplejson as json
except ImportError:
try:
import json
except ImportError:
try:
from django.utils import s... | 22 | 31.68 | 91 | 15 | 181 | python | [] | 0 | true | |
2024-11-19T02:04:43.969053+00:00 | 1,518,459,997,000 | 5d99b871112d565c91812d00cbf40319e90507cf | 3 | {
"blob_id": "5d99b871112d565c91812d00cbf40319e90507cf",
"branch_name": "refs/heads/master",
"committer_date": 1518459997000,
"content_id": "a4c3a9713d9b2a29497b1fe7b6112437624ccb83",
"detected_licenses": [
"MIT"
],
"directory_id": "0ae8b5fa69215be09001c27ae40a5059fb4e4189",
"extension": "py",
"fi... | 3.265625 | stackv2 | """P Neuron"""
import random
class PNeuron:
"""
each neuron has a unique nid,
connects to other neurons (targets),
follows a set of charge transformation rules,
can be an internal or output neuron with output neurons terminating the computation
"""
nid = 0
@staticmethod
def get_n... | 69 | 27.77 | 113 | 16 | 451 | python | [] | 0 | true | |
2024-11-19T02:04:44.328202+00:00 | 1,511,464,349,000 | 58c6a645e6b75bc7d6cc60be9526dcb6c638da54 | 3 | {
"blob_id": "58c6a645e6b75bc7d6cc60be9526dcb6c638da54",
"branch_name": "refs/heads/master",
"committer_date": 1511464349000,
"content_id": "e59d3557dd2edec3a2f8661e205eb9dc8dfa45b6",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "b202cef481853c4ebe89f5a7bea4881d0c7d7832",
"extension": "p... | 3.359375 | stackv2 | # coding=utf-8
__author__ = 'Sereni'
"""This module checks the task files filled in by evaluators and gives the number of correct answers.
Input: the keys file generated with tasks and a filled-in task file with unchanged structure:
task number:
original text
(optional) machine translation
reference translation with {g... | 76 | 31.51 | 113 | 17 | 596 | python | [] | 0 | true | |
2024-11-19T02:04:44.490149+00:00 | 1,436,945,700,000 | 0a729f63a83c080db387695055be1277ab869bf7 | 3 | {
"blob_id": "0a729f63a83c080db387695055be1277ab869bf7",
"branch_name": "refs/heads/master",
"committer_date": 1436945700000,
"content_id": "f69d07b80e0a572f430a2dc4d0f5407f4cbf7c8b",
"detected_licenses": [
"BSD-2-Clause"
],
"directory_id": "381ca98e1848960ce40c32f230d9b0fc1a2e0009",
"extension": "p... | 2.640625 | stackv2 | # -*- coding: utf-8 -*-
"""
Base classes for all TSIP packets
"""
import struct
from tsip.constants import DLE, DLE_STRUCT, ETX, ETX_STRUCT
def _extract_code_from_packet(packet):
code = struct.unpack('>B', packet[0])[0]
if code in [0x8f, 0x8e]:
return struct.unpack('>H', packet[0:2])[0]
return... | 82 | 18.79 | 59 | 13 | 406 | python | [] | 0 | true | |
2024-11-19T02:04:44.542551+00:00 | 1,675,335,449,000 | 26d1bebd82034db5b2caba60de188d95148e6b90 | 2 | {
"blob_id": "26d1bebd82034db5b2caba60de188d95148e6b90",
"branch_name": "refs/heads/main",
"committer_date": 1675337890000,
"content_id": "229cf0dd3102804e5b6549a39cdfddc0bb128dcc",
"detected_licenses": [
"MIT"
],
"directory_id": "8da2df96b51e8691059c99cd4bb09b6b59f5d31a",
"extension": "py",
"file... | 2.390625 | stackv2 | """
Multitask Model trained on the Unified DB
- The four datasets QM9, Alchemy, OE62, and HOPV, with the unified split
(split v2) are used.
- One set of molecular energies is trained: (HOMO, LUMO, gap) x (PBE0)
- The network has the Set2Set + molecule-level output module with six outputs,
Same as multitask_model_v0... | 59 | 29.86 | 83 | 11 | 496 | python | [] | 0 | true | |
2024-11-19T02:04:44.670702+00:00 | 1,626,706,234,000 | 5e37d19689bb854d497f495802da84001d848231 | 3 | {
"blob_id": "5e37d19689bb854d497f495802da84001d848231",
"branch_name": "refs/heads/main",
"committer_date": 1626706234000,
"content_id": "c89cf2944db28fce1ad66c99df60be7f0cd78f34",
"detected_licenses": [
"MIT"
],
"directory_id": "5e31c1eeec6abe32f5294b034e86709985720384",
"extension": "py",
"file... | 3.34375 | stackv2 | import sys
from data.point import Point
from asciimatics.event import KeyboardEvent
class InputServiceAscii:
"""Detects player input. The responsibility of the class of objects is to detect and communicate player keypresses.
Stereotype:
Service Provider
Attributes:
_screen (Screen): An A... | 37 | 29.7 | 119 | 14 | 291 | python | [] | 0 | true | |
2024-11-19T02:04:44.780700+00:00 | 1,431,544,408,000 | 13b3347b228545e8e65765bd5d64e5122f5052a1 | 3 | {
"blob_id": "13b3347b228545e8e65765bd5d64e5122f5052a1",
"branch_name": "refs/heads/master",
"committer_date": 1431544408000,
"content_id": "4d0f5c546d6fc24897eada99b9dc11c9e252c783",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "d864df0a56edb80b3d476b6fe17ac92f30276cea",
"extension": "py"... | 3.34375 | stackv2 | """Functions for working with data that does not contain a spatial component
This data will be the same for all observations in a given period, but will
vary over time, e.g. weather data, scheduled events, etc.
"""
import csv
from s3_utils import get_from_s3
def get_weather_data(path):
"""Helper function to get... | 36 | 27.94 | 87 | 13 | 249 | python | [] | 0 | true | |
2024-11-19T02:04:45.037821+00:00 | 1,636,207,959,000 | ca8f8709fb641a3198a12e164e4ca30d7157c134 | 3 | {
"blob_id": "ca8f8709fb641a3198a12e164e4ca30d7157c134",
"branch_name": "refs/heads/master",
"committer_date": 1636207959000,
"content_id": "423a33c6358a923a99b4af66c9986cca5dd7ea11",
"detected_licenses": [
"MIT"
],
"directory_id": "3a94afddd371ef0244ab13962479870f1d5a88f2",
"extension": "py",
"fi... | 2.65625 | stackv2 | import os
import sys
from sage.all import ZZ
from sage.all import continued_fraction
path = os.path.dirname(os.path.dirname(os.path.dirname(os.path.realpath(os.path.abspath(__file__)))))
if sys.path[1] != path:
sys.path.insert(1, path)
from attacks.factorization import known_phi
def attack(N, e):
"""
R... | 31 | 30.26 | 136 | 15 | 244 | python | [] | 0 | true | |
2024-11-19T02:04:45.277647+00:00 | 1,614,181,199,000 | f677b103bd34dae45164485af97d4b592aa9b8af | 3 | {
"blob_id": "f677b103bd34dae45164485af97d4b592aa9b8af",
"branch_name": "refs/heads/master",
"committer_date": 1614181199000,
"content_id": "6b5306b82372ffd200947a0c6ad90a42ea4ab48c",
"detected_licenses": [
"MIT",
"Python-2.0"
],
"directory_id": "50008b3b7fb7e14f793e92f5b27bf302112a3cb4",
"exten... | 2.625 | stackv2 | from __future__ import nested_scopes
import types
found='found'
def deepindex(sequence, goal):
"""deepindex(sequence,goal) -> index list"""
def helper(sequence,index_list):
for item in sequence:
if item==goal:
index_list.append(sequence.index(item))
raise fo... | 20 | 32.1 | 85 | 16 | 136 | python | [] | 0 | true | |
2024-11-19T02:04:45.696888+00:00 | 1,654,143,092,000 | 4d77d7b714b0ad08d00b95a745ab8aea267a992a | 2 | {
"blob_id": "4d77d7b714b0ad08d00b95a745ab8aea267a992a",
"branch_name": "refs/heads/master",
"committer_date": 1654143092000,
"content_id": "3a68991dac17842e26ccd94b7d64be7489fbfb06",
"detected_licenses": [
"BSD-2-Clause"
],
"directory_id": "b1213c567536bf490d45c0ab2de77ceeec9865e9",
"extension": "p... | 2.46875 | stackv2 | # Copyright 2019 VMware, Inc.
# SPDX-License-Identifier: BSD-2-Clause
import csv
from network_insight_sdk_generic_datasources.common.log import py_logger
import os
class CsvWriter(object):
CSV_EXTENSION = '.csv'
@staticmethod
def write(path, filename, table):
if table is None:
py_log... | 47 | 38.4 | 99 | 20 | 366 | python | [] | 0 | true | |
2024-11-19T02:04:45.759561+00:00 | 1,547,603,140,000 | c0929ed1294c1064674a841481a7591d5e2ca37c | 3 | {
"blob_id": "c0929ed1294c1064674a841481a7591d5e2ca37c",
"branch_name": "refs/heads/master",
"committer_date": 1547603140000,
"content_id": "1f6932286a85de5a9d96e42b7afb5dd85e029e34",
"detected_licenses": [
"MIT"
],
"directory_id": "6f0766bc05052e94787db575da36bdfbfbfad841",
"extension": "py",
"fi... | 3.203125 | stackv2 | # -*- coding: utf-8 -*-
"""
Created on Tue Mar 13 17:24:21 2018
@author: corey
"""
def vader_tweet(tweet):
'''returns vader polarity scores with the addition of a overall polarity score'''
results = analyzer.polarity_scores(tweet)
return results
def tweet_sentiments(user):
# Counter
counter = 1... | 54 | 26.94 | 85 | 14 | 329 | python | [] | 0 | true | |
2024-11-19T02:04:45.805835+00:00 | 1,535,704,760,000 | 283c158eb7cf095ba9509482a9fa2bf5dd711089 | 3 | {
"blob_id": "283c158eb7cf095ba9509482a9fa2bf5dd711089",
"branch_name": "refs/heads/master",
"committer_date": 1535704760000,
"content_id": "9f5d6b174214709d1cb322508d8efe9306aafb53",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "98fa1dbfe9a8b4e29624137ed3ef8ba7b46de939",
"extension": "py"... | 2.84375 | stackv2 | # Copyright (C) 2018 GuQiangJs.
# Licensed under Apache License 2.0 <see LICENSE file>
import pandas as pd
from pandas import read_excel
def get_stock_holdings(index: str):
""" 从 中证指数有限公司 获取指数的成分列表
Args:
index: 指数代码
Returns:
``pandas.DataFrame``:
Examples:
.. code-block:: p... | 79 | 28.29 | 87 | 11 | 713 | python | [] | 0 | true | |
2024-11-19T02:04:46.053389+00:00 | 1,617,720,893,000 | 647d96632be9659812ac379f4046ed921f8ef8b9 | 4 | {
"blob_id": "647d96632be9659812ac379f4046ed921f8ef8b9",
"branch_name": "refs/heads/main",
"committer_date": 1617720893000,
"content_id": "49da52f46f0a75cc11e1a6afd12ef0e2bcb651c5",
"detected_licenses": [
"MIT"
],
"directory_id": "cdade1a368a069ab6ec61104c831d0760e87c141",
"extension": "py",
"file... | 3.6875 | stackv2 | # Lesson 3.10 - Types of "join"
import pandas as pd
def test_run():
start_date = '2010-01-22'
end_date = '2010-01-26'
dates = pd.date_range(start_date, end_date)
df1 = pd.DataFrame(index=dates)
dfSPY = pd.read_csv("C:/Code/Python/Udacity/data/SPY.csv",
index_col='Date',parse_dates=True,
... | 18 | 25.5 | 62 | 11 | 157 | python | [] | 0 | true | |
2024-11-19T02:04:46.103228+00:00 | 1,634,923,031,000 | ace132019b9e9bf7b0f9ad0707898315919937c3 | 3 | {
"blob_id": "ace132019b9e9bf7b0f9ad0707898315919937c3",
"branch_name": "refs/heads/master",
"committer_date": 1634923031000,
"content_id": "3b69e328d86be19b75e92480fb3a9d46023e9197",
"detected_licenses": [
"MIT"
],
"directory_id": "2a61b02c26e77686e38cd9039e6f4b0530ddb7c9",
"extension": "py",
"fi... | 2.703125 | stackv2 | import cv2
from cv_bridge import CvBridge
import VisionExtensions
import numpy as np
from .candidate import Candidate, CandidateFinder
import itertools
import random
import rospy
from .live_fcnn_03 import FCNN03
class FcnnHandler(CandidateFinder):
"""
The :class:`.FcnnHandler` handles Fully Convolutional Neur... | 267 | 39.7 | 156 | 20 | 2,506 | python | [] | 0 | true | |
2024-11-19T02:04:46.321532+00:00 | 1,587,749,519,000 | 713a1cd2635af5e7b47581ab73e836a259faddc2 | 2 | {
"blob_id": "713a1cd2635af5e7b47581ab73e836a259faddc2",
"branch_name": "refs/heads/master",
"committer_date": 1587749519000,
"content_id": "ba1afbe07429fe75860621c3c051ab62df3b6877",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "c5c49a82f049057156d23095e4578fa5ce886ed6",
"extension": "p... | 2.375 | stackv2 | import argparse
import h5py
import numpy as np
parser = argparse.ArgumentParser(description='Create a test file for H5Dread() profiling.')
parser.add_argument('-c', action='store_true', help='Create a chunked dataset.')
parser.add_argument('-F', action='store_true', help='Use the latest file format')
parser.add_argume... | 23 | 30.61 | 91 | 11 | 202 | python | [] | 0 | true | |
2024-11-19T02:04:46.368812+00:00 | 1,627,355,724,000 | 9f025816e5cfa295dec885100e04dcfb22f83d33 | 4 | {
"blob_id": "9f025816e5cfa295dec885100e04dcfb22f83d33",
"branch_name": "refs/heads/main",
"committer_date": 1627355724000,
"content_id": "1ebcb9c59cdf88f100d7352b3c0c391d6a55e793",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "d7e17ff5160cc61a07d82ec450f240e4a6dde093",
"extension": "py",
... | 3.75 | stackv2 | """
Бустрая сортировка методом разделяй и влавствуй
Базовый случай:
[] -> []
[x] -> [x]
"""
import unittest
def my_quicksort(arr):
if len(arr) < 2:
return arr
else:
pivot = arr[0]
less = [i for i in arr[1:] if i <= pivot]
greater = [i for i in arr[1:] if i > pivot]
r... | 40 | 20.68 | 67 | 13 | 265 | python | [] | 0 | true | |
2024-11-19T02:04:46.476904+00:00 | 1,548,799,199,000 | e7571af783e0471181da9807b051cb8d1eddb8a6 | 2 | {
"blob_id": "e7571af783e0471181da9807b051cb8d1eddb8a6",
"branch_name": "refs/heads/master",
"committer_date": 1548799199000,
"content_id": "8dc4bb7af4b7f7f4f103615326f0778888cc0854",
"detected_licenses": [
"MIT"
],
"directory_id": "caad906678ae245cdfc9d7015fa5ea84881f370e",
"extension": "py",
"fi... | 2.484375 | stackv2 | #####################################################################################
# MIT License #
# #
# Copyright (C) 2019 Charly Lamothe ... | 225 | 35.97 | 135 | 16 | 1,751 | python | [] | 0 | true | |
2024-11-19T02:04:46.594698+00:00 | 1,528,026,015,000 | d45e5cad85099467205f84c0726314ccea1e6040 | 3 | {
"blob_id": "d45e5cad85099467205f84c0726314ccea1e6040",
"branch_name": "refs/heads/master",
"committer_date": 1528026015000,
"content_id": "489786d4b2cc81884fe850cbc19de7dec1a53b2f",
"detected_licenses": [
"MIT"
],
"directory_id": "0a0790d8e587d95200b5d7097f361315562045d1",
"extension": "py",
"fi... | 2.6875 | stackv2 | # -*- coding: utf-8 -*-
"""
Module to get and parse the product info on Amazon
"""
import requests
from urllib.parse import urljoin
from bs4 import BeautifulSoup
_BASE_URL = "https://www.amazon.com/"
_DEFAULT_BEAUTIFULSOUP_PARSER = "html.parser"
CSS_SELECTORS_1 = {
"product": "#resultItems > li",
"title": "a ... | 142 | 40.65 | 79 | 22 | 1,319 | python | [] | 0 | true | |
2024-11-19T02:04:46.695230+00:00 | 1,692,767,210,000 | bdd72aed9d2e9717c1ec2a5cce0ed684c3a087fc | 3 | {
"blob_id": "bdd72aed9d2e9717c1ec2a5cce0ed684c3a087fc",
"branch_name": "refs/heads/main",
"committer_date": 1692767210000,
"content_id": "86413eaf893e0776ce8a2a2be90939d0e62ad3c1",
"detected_licenses": [
"MIT"
],
"directory_id": "8fafdc8ec12d895cd34299ecc1568c046f8c5252",
"extension": "py",
"file... | 2.984375 | stackv2 | ## Brian Blaylock
## September 22, 2020
"""
===========
Other Tools
===========
Other tools for handeling NOAA GOES data files.
"""
import numpy as np
import cartopy.crs as ccrs
import warnings
try:
import metpy # Need accessors to get projection info.
except:
# Not sure why sphinx can't import metpy??
... | 192 | 35.18 | 87 | 18 | 1,846 | python | [] | 0 | true | |
2024-11-19T02:04:46.755414+00:00 | 1,498,497,881,000 | d7c3c4de74f12366797d0f32d6523e08d565dae5 | 2 | {
"blob_id": "d7c3c4de74f12366797d0f32d6523e08d565dae5",
"branch_name": "refs/heads/master",
"committer_date": 1498497881000,
"content_id": "11458a5d8e2427d645783256d65e4443974943f9",
"detected_licenses": [
"MIT"
],
"directory_id": "fa1ab49fad5e9fa3f36577c74561fccd216a0162",
"extension": "py",
"fi... | 2.5 | stackv2 | from .verificate_diff import VerificateDiff
class Verificate:
def __init__(self, inputfile, outputfile, correctoutput, isolator=None):
Verificator = VerificateDiff
self._verif = Verificator(inputfile, outputfile, correctoutput)
self._isol = isolator
def verificate(self):
self... | 19 | 25.68 | 77 | 11 | 123 | python | [] | 0 | true | |
2024-11-19T02:04:46.813903+00:00 | 1,586,720,863,000 | 4656069eb49f3b9fa123b13dc23909b5148b5811 | 2 | {
"blob_id": "4656069eb49f3b9fa123b13dc23909b5148b5811",
"branch_name": "refs/heads/master",
"committer_date": 1586720863000,
"content_id": "2a1e04b2d26d0fb2f95b70d3768ac865d56efcbf",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "630cdfde8f46fbce1545f8f98d85335d316ad64f",
"extension": "py"... | 2.484375 | stackv2 | _PATH = '../../../datasets/'
import os, sys
if _PATH not in sys.path:
sys.path.insert(0, _PATH)
import tensorflow as tf
import tensorflow_datasets as tfds
from trainer.datasets.utils import normalize, random_jitter
import numpy as np
from skimage.transform import radon,iradon
import glob
def deeplesion(config_na... | 193 | 35.81 | 169 | 19 | 1,877 | python | [] | 0 | true | |
2024-11-19T02:04:47.102375+00:00 | 1,367,830,479,000 | e075c0d05cc8c15c8c1b9b00893ffa38902996c7 | 3 | {
"blob_id": "e075c0d05cc8c15c8c1b9b00893ffa38902996c7",
"branch_name": "refs/heads/master",
"committer_date": 1367830479000,
"content_id": "c859373415818e62df0f198599381cd9f61192ff",
"detected_licenses": [
"MIT"
],
"directory_id": "06073e2f5564ac55ceeda35bd14df569b73b27dd",
"extension": "py",
"fi... | 2.8125 | stackv2 | # coding=utf-8
import sublime
import sublime_plugin
import webbrowser
import urllib
class GoogleItCommand(sublime_plugin.TextCommand):
def run(self, edit, include_language=False):
"""Google the selected text.
"""
view = self.view
# Get the highlighted string to search for
selecte... | 32 | 31.88 | 103 | 16 | 226 | python | [] | 0 | true | |
2024-11-19T02:04:47.593491+00:00 | 1,593,544,242,000 | 88eb91e657a4dd8d319cf08ba513d5716d00fb0e | 2 | {
"blob_id": "88eb91e657a4dd8d319cf08ba513d5716d00fb0e",
"branch_name": "refs/heads/master",
"committer_date": 1593544242000,
"content_id": "519618ca9251a6a2246749d54615734e6b17fff2",
"detected_licenses": [
"MIT"
],
"directory_id": "5184f6470f9cf1e45a35330880e54df6267f8de6",
"extension": "py",
"fi... | 2.375 | stackv2 | ## regular imports
import sys, os, socket, qdarkstyle
## PyQt5 imports
from PyQt5.QtWidgets import *
from PyQt5.QtCore import *
from PyQt5 import QtCore
from PyQt5 import QtGui
from PyQt5.QtGui import *
class ConnectionHandler:
def __init__(self, port, ID):
self.s = socket.socket(socket.AF_INET, socket.... | 175 | 36.43 | 101 | 16 | 1,373 | python | [] | 0 | true | |
2024-11-19T02:04:47.787799+00:00 | 1,538,497,930,000 | 237cd10becce29ca02e20045027b95960b2065e6 | 3 | {
"blob_id": "237cd10becce29ca02e20045027b95960b2065e6",
"branch_name": "refs/heads/master",
"committer_date": 1538497930000,
"content_id": "36c74e86570238b389a812855ea421de9a3baf93",
"detected_licenses": [
"BSD-2-Clause-Views"
],
"directory_id": "e3ed3f09396bbce16a5ec571ea0ec26a4193ad2e",
"extensio... | 3.421875 | stackv2 | import os
from collections import OrderedDict
from shutil import rmtree
from artemis.general.display import surround_with_header
from artemis.general.should_be_builtins import izip_equal
from six.moves import input
def crawl_directory(directory, ignore_hidden = True):
"""
Given a directory, return a dict repr... | 195 | 34.52 | 136 | 20 | 1,526 | python | [] | 0 | true | |
2024-11-19T02:04:47.854303+00:00 | 1,397,230,323,000 | 9b65cd6ee19eaa0c58dd0164c858ca2dc11f6d95 | 3 | {
"blob_id": "9b65cd6ee19eaa0c58dd0164c858ca2dc11f6d95",
"branch_name": "refs/heads/master",
"committer_date": 1397230323000,
"content_id": "c9c6774c1291471684480d8c5a10ab0c0b817a3c",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "d60ce42682dfab71a308844da0adf952238bed90",
"extension": "py"... | 2.9375 | stackv2 | import nltk
from xml.dom import minidom
import csv
import re
from nltk.stem.snowball import SnowballStemmer
class PreprocessUtils():
def __init__(self):
self.porterStemmer = nltk.PorterStemmer()
self.stopwordsList = nltk.corpus.stopwords.words("english")
self.wordNetLemmatizer = nltk.WordN... | 77 | 38.83 | 126 | 20 | 783 | python | [] | 0 | true | |
2024-11-19T02:04:48.011520+00:00 | 1,583,245,369,000 | df58f019f5bb5f167d07ca01126d0f085ec4ae20 | 3 | {
"blob_id": "df58f019f5bb5f167d07ca01126d0f085ec4ae20",
"branch_name": "refs/heads/master",
"committer_date": 1583245369000,
"content_id": "bbcd7f71d84c8897ef010518f0a21d0b3f33ddb2",
"detected_licenses": [
"MIT"
],
"directory_id": "979793b6e47a759aff2389d44f13c8deef129ac7",
"extension": "py",
"fi... | 2.953125 | stackv2 | import time
import numpy as np
from dataclasses import dataclass
@dataclass
class ObjectDetection:
coord: np.array
conf: float
crop: np.array
cls: int
timestamp: float = time.time()
"""
Representation of a detection on an Image. This is model agnostic.
"""
@staticmethod
def fro... | 35 | 24.8 | 70 | 16 | 239 | python | [] | 0 | true | |
2024-11-19T02:04:48.118235+00:00 | 1,624,777,150,000 | f53a5ac44af80c16bb6e4800f24db5f33c3cfe2a | 3 | {
"blob_id": "f53a5ac44af80c16bb6e4800f24db5f33c3cfe2a",
"branch_name": "refs/heads/master",
"committer_date": 1624777150000,
"content_id": "64db9318ff7e49773437ad90a667e8c219979f49",
"detected_licenses": [
"Apache-2.0"
],
"directory_id": "b6e5066821355294a9174be80af90312a071a5c2",
"extension": "py"... | 2.796875 | stackv2 | import subprocess
from os import path
def __getattr__(name):
name = name.replace('_', '-')
def callback():
file_name = 'scripts/%s.sh' % name
if path.exists(file_name):
p = subprocess.Popen(['bash', file_name])
p.wait()
out, err = p.communicate()
... | 18 | 25.44 | 64 | 15 | 102 | python | [] | 0 | true | |
2024-11-19T02:04:48.289951+00:00 | 1,609,658,252,000 | abd4b30b015843def93eaaee2f22587abae2b8c7 | 3 | {
"blob_id": "abd4b30b015843def93eaaee2f22587abae2b8c7",
"branch_name": "refs/heads/master",
"committer_date": 1609658252000,
"content_id": "0d8001e2a6c5b01cc3a4dd82bd1dab80be1fe626",
"detected_licenses": [
"MIT"
],
"directory_id": "983f118d7a430d2a0e15f7c773454933d76f42bc",
"extension": "py",
"fi... | 3.421875 | stackv2 | """
compare recpie API
"""
def is_comparable(ask_ing, user_ing):
"""tell if the ingredients are comparable
Args:
ask_ing: ingredient from recipe
user_ing: ingredient from user
Returns:
return True if they have same name and same quantity_unit
"""
if (ask_ing["name"] == user... | 85 | 23.69 | 79 | 12 | 496 | python | [] | 0 | true | |
2024-11-19T02:04:48.492938+00:00 | 1,614,286,382,000 | 8f71a5345717261e6e4dc957f87f1e7996182dfb | 2 | {
"blob_id": "8f71a5345717261e6e4dc957f87f1e7996182dfb",
"branch_name": "refs/heads/main",
"committer_date": 1614286382000,
"content_id": "15edb98beda3fdd1c48f8c6922464840c9f66ca5",
"detected_licenses": [
"MIT"
],
"directory_id": "1c8bcd2d8e129a92e3328f47d2a452814c033327",
"extension": "py",
"file... | 2.5 | stackv2 | # This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
# import numpy as np # linear algebra
# import pandas as pd # data processing, CSV file... | 89 | 35.67 | 115 | 12 | 825 | python | [] | 0 | true | |
2024-11-19T02:04:48.554613+00:00 | 1,350,258,440,000 | 075a213e79ae5c6e70fac5387ebf5c0eee0bfbad | 3 | {
"blob_id": "075a213e79ae5c6e70fac5387ebf5c0eee0bfbad",
"branch_name": "refs/heads/master",
"committer_date": 1350258440000,
"content_id": "fb6df17f5cd270a995f2a7bedd08a609d85d6a50",
"detected_licenses": [
"BSD-3-Clause"
],
"directory_id": "7dbe7a85f3b3ee3d163bb1864233a2cea084f1bd",
"extension": "p... | 2.53125 | stackv2 | from rupypy.module import ClassDef
from rupypy.objects.objectobject import W_Object
class W_NumericObject(W_Object):
classdef = ClassDef("Numeric", W_Object.classdef)
@classdef.method("<=>")
def method_comparator(self, space, w_other):
if self == w_other:
return space.newint(0)
... | 21 | 28.43 | 56 | 13 | 147 | python | [] | 0 | true | |
2024-11-19T02:04:48.604029+00:00 | 1,640,304,154,000 | e3e40a06c3f12d7e57b53aa99e87de667fe9d5a2 | 2 | {
"blob_id": "e3e40a06c3f12d7e57b53aa99e87de667fe9d5a2",
"branch_name": "refs/heads/master",
"committer_date": 1640304154000,
"content_id": "fb95c739144c7e6524d50caee7d82f8fe99e2ca1",
"detected_licenses": [
"MIT"
],
"directory_id": "40218b64840f4eec1866e33300a8395bdfa4c33b",
"extension": "py",
"fi... | 2.40625 | stackv2 | #!/usr/bin/env python
# -*- coding: utf-8 -*-
import numpy as np
import matplotlib.pyplot as plt
import time
import random
import sys
from mindset.MindSet import *
class TestGraphics():
def __init__( self ):
pass
def run( self ):
#sys.setcheckinterval( 100 )
attentionESense = [0]*10... | 101 | 30.16 | 68 | 14 | 923 | python | [] | 0 | true |
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