code stringlengths 2k 1.04M | repo_path stringlengths 5 517 | parsed_code stringlengths 0 1.04M | quality_prob float64 0.02 0.95 | learning_prob float64 0.02 0.93 |
|---|---|---|---|---|
import json
from json.decoder import JSONDecodeError
import jwt
import requests
from flask import request, current_app, jsonify, g
from jwt import InvalidSignatureError, InvalidAudienceError, DecodeError
from requests.exceptions import (
ConnectionError,
InvalidURL,
SSLError,
HTTPError
)
from api.erro... | code/api/utils.py | import json
from json.decoder import JSONDecodeError
import jwt
import requests
from flask import request, current_app, jsonify, g
from jwt import InvalidSignatureError, InvalidAudienceError, DecodeError
from requests.exceptions import (
ConnectionError,
InvalidURL,
SSLError,
HTTPError
)
from api.erro... | 0.414899 | 0.166134 |
class RenderTree(object):
"""A tree interface where the only responsibility of subclasses is to define
what it means to render. Calling str on a RenderTree will implictly call render."""
def __str__(self):
return self.render()
def render(self, prefix=''):
raise NotImplemented("Implemen... | pcol.py | class RenderTree(object):
"""A tree interface where the only responsibility of subclasses is to define
what it means to render. Calling str on a RenderTree will implictly call render."""
def __str__(self):
return self.render()
def render(self, prefix=''):
raise NotImplemented("Implemen... | 0.942718 | 0.638413 |
from OpenGL.GL import *
from OpenGL.GLUT import *
from OpenGL.GLU import *
import math
import random
colors = []
width = height = 900
eps: int
iterations: int
umin: float
umax: float
vmin: float
vmax: float
method: int
creal: float
cimag: float
window: GLint
def myReshape(width, height):
glMatrixMode(GL_PROJECTI... | lab8/zad8.py | from OpenGL.GL import *
from OpenGL.GLUT import *
from OpenGL.GLU import *
import math
import random
colors = []
width = height = 900
eps: int
iterations: int
umin: float
umax: float
vmin: float
vmax: float
method: int
creal: float
cimag: float
window: GLint
def myReshape(width, height):
glMatrixMode(GL_PROJECTI... | 0.389198 | 0.180053 |
import os
from test.matcher import JSPECTestMatcher
os.environ["TEST_OBJECT"] = '{"hello": "world"}'
os.environ["TEST_ARRAY"] = '["hello", "world", 123]'
os.environ["TEST_STRING"] = '"Hello World!"'
os.environ["TEST_INT"] = '456'
os.environ["TEST_REAL"] = '7.89'
os.environ["TEST_BOOLEAN"] = 'true'
os.environ["TEST_NU... | test/matcher/macro.py | import os
from test.matcher import JSPECTestMatcher
os.environ["TEST_OBJECT"] = '{"hello": "world"}'
os.environ["TEST_ARRAY"] = '["hello", "world", 123]'
os.environ["TEST_STRING"] = '"Hello World!"'
os.environ["TEST_INT"] = '456'
os.environ["TEST_REAL"] = '7.89'
os.environ["TEST_BOOLEAN"] = 'true'
os.environ["TEST_NU... | 0.617051 | 0.405331 |
import frappe
from frappe.model.document import Document
def wh_qnt(qt,warehouse):
t=0
wh_qt=frappe.db.get_value("WH",{"wh_name":warehouse},"wh_qt")
if(wh_qt-qt>=0):
#frappe.db.set_value("WH",{"wh_name":warehouse},"wh_qt",wh_qt-qt)
t=(wh_qt-qt)
return t
def add(warehouse,item,qt):
C_WH_doc=frappe.get_doc... | whms/whms/doctype/stock_entry/stock_entry.py |
import frappe
from frappe.model.document import Document
def wh_qnt(qt,warehouse):
t=0
wh_qt=frappe.db.get_value("WH",{"wh_name":warehouse},"wh_qt")
if(wh_qt-qt>=0):
#frappe.db.set_value("WH",{"wh_name":warehouse},"wh_qt",wh_qt-qt)
t=(wh_qt-qt)
return t
def add(warehouse,item,qt):
C_WH_doc=frappe.get_doc... | 0.143128 | 0.105856 |
import datetime
import json
import os
import sys
import tempfile
import boto3
import torch
import yaml
from sagemaker_inference import content_types, errors
# path munging to access the neighboring YAEDP repo
HERE = os.path.dirname(os.path.realpath(__file__))
YAEDP_ROOT = os.path.join(HERE, 'Yet-Another-EfficientDet... | storyblocks/object_detection/model/code/inference.py | import datetime
import json
import os
import sys
import tempfile
import boto3
import torch
import yaml
from sagemaker_inference import content_types, errors
# path munging to access the neighboring YAEDP repo
HERE = os.path.dirname(os.path.realpath(__file__))
YAEDP_ROOT = os.path.join(HERE, 'Yet-Another-EfficientDet... | 0.534127 | 0.153486 |
import os
os.system('pip install colorama')
os.system('pip install hashlib')
time.sleep(2)
import hashlib
import colorama
import time
from bunner import *
import random
banner()
os.system('termux-open-url https://t.me/elf_security_cyber')
time.sleep(5)
while True:
print(f"{colorama.Fore.RED}[{c... | hasher.py | import os
os.system('pip install colorama')
os.system('pip install hashlib')
time.sleep(2)
import hashlib
import colorama
import time
from bunner import *
import random
banner()
os.system('termux-open-url https://t.me/elf_security_cyber')
time.sleep(5)
while True:
print(f"{colorama.Fore.RED}[{c... | 0.07164 | 0.492249 |
from utils import *
def imgs_from_category(dataset):
filename = os.path.join(set_dir, dataset + ".txt")
df = pd.read_csv(
filename,
delim_whitespace=True,
header=None,
names=['filename'])
return df
def imgs_from_category_as_list(dataset):
df = imgs_from_category(datase... | src/texture_analisys/loadingDataset.py | from utils import *
def imgs_from_category(dataset):
filename = os.path.join(set_dir, dataset + ".txt")
df = pd.read_csv(
filename,
delim_whitespace=True,
header=None,
names=['filename'])
return df
def imgs_from_category_as_list(dataset):
df = imgs_from_category(datase... | 0.114233 | 0.142799 |
def parse_officer(line):
"""
Summary: Parse a line of officer data from the FTP
Inputs: line of raw ftp data
Outputs: a dictionary of data parsed from the line
"""
results = dict()
results['appointed_to_company_number'] = line[0:8]
results['record_type'] = line[8]
results['a... | src/parse_ftp.py | def parse_officer(line):
"""
Summary: Parse a line of officer data from the FTP
Inputs: line of raw ftp data
Outputs: a dictionary of data parsed from the line
"""
results = dict()
results['appointed_to_company_number'] = line[0:8]
results['record_type'] = line[8]
results['a... | 0.481698 | 0.375993 |
import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../varity')))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../varity/common')))
import varity
import cfg
import random
import shutil
import socket
import json
def configure():
cfg.MAX_EX... | tests/test_driver.py | import os
import sys
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../varity')))
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), '../varity/common')))
import varity
import cfg
import random
import shutil
import socket
import json
def configure():
cfg.MAX_EX... | 0.158923 | 0.121399 |
import pytest
from app.models import Resource, Language, Category
from tests.conftest import module_client, module_db
from configs import PaginatorConfig
# TODO: We need negative unit tests (what happens when bad data is sent)
def test_get_resources(module_client, module_db):
client = module_client
response ... | tests/unit/test_persistent_routes.py | import pytest
from app.models import Resource, Language, Category
from tests.conftest import module_client, module_db
from configs import PaginatorConfig
# TODO: We need negative unit tests (what happens when bad data is sent)
def test_get_resources(module_client, module_db):
client = module_client
response ... | 0.245447 | 0.273093 |
__copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Iterator, Any
from jina import Document
from jina.executors.indexers.keyvalue import BinaryPbIndexer
class LevelDBIndexer(BinaryPbIndexer):
"""
:class:`LevelDBIndexer` use `Leve... | indexers/keyvalue/LevelDBIndexer/__init__.py | __copyright__ = "Copyright (c) 2020 Jina AI Limited. All rights reserved."
__license__ = "Apache-2.0"
from typing import Optional, Iterator, Any
from jina import Document
from jina.executors.indexers.keyvalue import BinaryPbIndexer
class LevelDBIndexer(BinaryPbIndexer):
"""
:class:`LevelDBIndexer` use `Leve... | 0.886807 | 0.133868 |
import csv
import pyproj
class TileMapper:
"""Tile Mapper loads a CSV file containing various geographic tiles and """
tile_list = []
PIXELS = 256
def __init__(self,pixels=256):
"""pixels specifies the tile resolution
Program assumes that we're dealing with square tiles"""
... | tools/tile_mapper/tile_mapper.py | import csv
import pyproj
class TileMapper:
"""Tile Mapper loads a CSV file containing various geographic tiles and """
tile_list = []
PIXELS = 256
def __init__(self,pixels=256):
"""pixels specifies the tile resolution
Program assumes that we're dealing with square tiles"""
... | 0.573081 | 0.575767 |
from PySide2 import QtGui, QtCore, QtWidgets
from .signal_def import signal_def
from .signal import Signal
from .scrollbar import ScrollBar
from .xaxis import XAxis
from .settings_widget import SettingsWidget
from .font_resizer import FontResizer
from .ymarker_manager import YMarkerManager
from joulescope_ui.file_dial... | joulescope_ui/widgets/waveform/waveform.py |
from PySide2 import QtGui, QtCore, QtWidgets
from .signal_def import signal_def
from .signal import Signal
from .scrollbar import ScrollBar
from .xaxis import XAxis
from .settings_widget import SettingsWidget
from .font_resizer import FontResizer
from .ymarker_manager import YMarkerManager
from joulescope_ui.file_dial... | 0.715523 | 0.075653 |
import datetime
from . import models
class ItemAlreadyExists(Exception):
pass
class ItemDoesNotExist(Exception):
pass
class DiscountMixin(object):
pass
class ShopBasketContainer(object):
""" Class for operate basket in session.
Using request.session dict, where
find BASKET_ID... | src/basket/basket.py |
import datetime
from . import models
class ItemAlreadyExists(Exception):
pass
class ItemDoesNotExist(Exception):
pass
class DiscountMixin(object):
pass
class ShopBasketContainer(object):
""" Class for operate basket in session.
Using request.session dict, where
find BASKET_ID... | 0.501465 | 0.223864 |
import matplotlib.pyplot as plt
import numpy as np
from .base import BaseProcessor
from ..plotter import OneDimPlotter, TwoDimPlotter, cdf_pdf
class ImageProcessor(BaseProcessor):
""" Process the information related to image, get several statistical distribution.
Args:
data (dict): Data to be proces... | volkscv/analyzer/statistics/processor/image_processor.py | import matplotlib.pyplot as plt
import numpy as np
from .base import BaseProcessor
from ..plotter import OneDimPlotter, TwoDimPlotter, cdf_pdf
class ImageProcessor(BaseProcessor):
""" Process the information related to image, get several statistical distribution.
Args:
data (dict): Data to be proces... | 0.907866 | 0.564279 |
import argparse
import fileinput
import re
from typing import Dict, Iterator, Sequence, Tuple
from itertools import chain, permutations
def _all_sorted_subsets(s: Sequence[str]) -> Iterator[Tuple[str, ...]]:
for i in range(1, len(s) + 1):
for p in permutations(s, i):
yield p
def syn_effects... | tools/generate.py |
import argparse
import fileinput
import re
from typing import Dict, Iterator, Sequence, Tuple
from itertools import chain, permutations
def _all_sorted_subsets(s: Sequence[str]) -> Iterator[Tuple[str, ...]]:
for i in range(1, len(s) + 1):
for p in permutations(s, i):
yield p
def syn_effects... | 0.519765 | 0.161485 |
from FichaDB import FichaDB
class Ficha(object):
def __init__(self, lista=None):
if lista is None:
self.lista = [] * 129
self.info = {}
def insertFicha(self):
banco = FichaDB()
try:
c = banco.conexao.cursor()
c.execute(" insert into fich... | Ficha.py | from FichaDB import FichaDB
class Ficha(object):
def __init__(self, lista=None):
if lista is None:
self.lista = [] * 129
self.info = {}
def insertFicha(self):
banco = FichaDB()
try:
c = banco.conexao.cursor()
c.execute(" insert into fich... | 0.085294 | 0.115063 |
import sys
import struct
import marshal
from osmxmlparser import Node, Way, OSMXMLParser
def write_obj(fp, obj):
b = marshal.dumps(obj)
fp.write('+'+struct.pack('<I', len(b))+b)
return
def getname(names):
name = None
for (k,v) in names:
if k == 'name' and name is None:
name = v... | parseosm.py | import sys
import struct
import marshal
from osmxmlparser import Node, Way, OSMXMLParser
def write_obj(fp, obj):
b = marshal.dumps(obj)
fp.write('+'+struct.pack('<I', len(b))+b)
return
def getname(names):
name = None
for (k,v) in names:
if k == 'name' and name is None:
name = v... | 0.15633 | 0.06389 |
from app import db, login_manager
from flask_login import UserMixin
from datetime import datetime
@login_manager.user_loader
def load_user(id):
return User.query.get(int(id))
class Marking(db.Model):
__tablename__ = "marking"
marker_id = db.Column(db.Integer, db.ForeignKey('user.id'), primary_key=True)
marked_id ... | app/models.py | from app import db, login_manager
from flask_login import UserMixin
from datetime import datetime
@login_manager.user_loader
def load_user(id):
return User.query.get(int(id))
class Marking(db.Model):
__tablename__ = "marking"
marker_id = db.Column(db.Integer, db.ForeignKey('user.id'), primary_key=True)
marked_id ... | 0.377885 | 0.043711 |
from __future__ import unicode_literals
import django.core.validators
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
("contenttypes", "0001_initial"),
... | apps/shop/migrations/0001_initial.py | from __future__ import unicode_literals
import django.core.validators
from django.conf import settings
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
("contenttypes", "0001_initial"),
... | 0.594198 | 0.147709 |
import common
def FullOTA_InstallEnd(info):
OTA_InstallEnd(info, False)
def IncrementalOTA_InstallEnd(info):
OTA_InstallEnd(info, True)
def AddImageOnly(info, basename, incremental, firmware):
if incremental:
input_zip = info.source_zip
else:
input_zip = info.input_zip
if firmware:
data = inpu... | releasetools.py |
import common
def FullOTA_InstallEnd(info):
OTA_InstallEnd(info, False)
def IncrementalOTA_InstallEnd(info):
OTA_InstallEnd(info, True)
def AddImageOnly(info, basename, incremental, firmware):
if incremental:
input_zip = info.source_zip
else:
input_zip = info.input_zip
if firmware:
data = inpu... | 0.159512 | 0.16896 |
import numpy as n, pylab as p
from scipy import stats as st
a=st.norm(0,1)
b=st.norm(0.1,1)
domain=n.linspace(-4,4,10000)
avals=a.cdf(domain)
bvals=b.cdf(domain)
diffN=n.abs(avals-bvals).max()
a=st.norm(0,1)
b=st.norm(0,1.2)
domain=n.linspace(-4,4,10000)
avals=a.cdf(domain)
bvals=b.cdf(domain)
diffN2=n.abs(avals-bvals... | tests/testTheoreticalCumulatives.py | import numpy as n, pylab as p
from scipy import stats as st
a=st.norm(0,1)
b=st.norm(0.1,1)
domain=n.linspace(-4,4,10000)
avals=a.cdf(domain)
bvals=b.cdf(domain)
diffN=n.abs(avals-bvals).max()
a=st.norm(0,1)
b=st.norm(0,1.2)
domain=n.linspace(-4,4,10000)
avals=a.cdf(domain)
bvals=b.cdf(domain)
diffN2=n.abs(avals-bvals... | 0.344333 | 0.383295 |
import time
everything_start_time = time.time()
import os
import subprocess
import json
import argparse
import cv2
import numpy
import SRers
parser = argparse.ArgumentParser()
# Input/output file
parser.add_argument('-i', '--input', # Input file
type=str,
help='path of vid... | run.py | import time
everything_start_time = time.time()
import os
import subprocess
import json
import argparse
import cv2
import numpy
import SRers
parser = argparse.ArgumentParser()
# Input/output file
parser.add_argument('-i', '--input', # Input file
type=str,
help='path of vid... | 0.334263 | 0.071786 |
import torch
from nitorch.core import py, utils
from nitorch import io, spatial
from .cnn import NeuriteUNet
from .norm import BatchNorm
from .conv import Conv
from .spatial import GridExp, GridResize, GridPull
from ..base import Module
class SynthUNet(NeuriteUNet):
"""Base for SynthStuff networks
This archi... | nitorch/nn/modules/pretrained.py | import torch
from nitorch.core import py, utils
from nitorch import io, spatial
from .cnn import NeuriteUNet
from .norm import BatchNorm
from .conv import Conv
from .spatial import GridExp, GridResize, GridPull
from ..base import Module
class SynthUNet(NeuriteUNet):
"""Base for SynthStuff networks
This archi... | 0.868674 | 0.325641 |
# Atom regex : ([A-Za-z]{1,})
dictionary = {
"adjectives": [ # Source : https://enchantedlearning.com/wordlist/adjectivesforpeople.shtml
"able",
# "abnormal",
"adventurous",
"affectionate",
"agile",
"agreeable",
"alert",
"amazing",
... | sami/lib/dictionary.py |
# Atom regex : ([A-Za-z]{1,})
dictionary = {
"adjectives": [ # Source : https://enchantedlearning.com/wordlist/adjectivesforpeople.shtml
"able",
# "abnormal",
"adventurous",
"affectionate",
"agile",
"agreeable",
"alert",
"amazing",
... | 0.263126 | 0.260436 |
"""Testing Module db.cls_db_core."""
import os
import pathlib
import shutil
import cfg.cls_setup
import cfg.glob
import db.cls_db_core
import db.cls_run
import pytest
import sqlalchemy
import utils
import dcr
# -----------------------------------------------------------------------------
# Constants & Globals.
# ---... | tests/test_db_cls_db_core.py | """Testing Module db.cls_db_core."""
import os
import pathlib
import shutil
import cfg.cls_setup
import cfg.glob
import db.cls_db_core
import db.cls_run
import pytest
import sqlalchemy
import utils
import dcr
# -----------------------------------------------------------------------------
# Constants & Globals.
# ---... | 0.586641 | 0.203193 |
from elastichq.globals import REQUEST_TIMEOUT
from .ConnectionService import ConnectionService
class QueryService:
def run_query(self, cluster_name, index_name, query_json):
connection = ConnectionService().get_connection(cluster_name)
if connection.version.startswith("2"):
from elas... | elastichq/service/QueryService.py | from elastichq.globals import REQUEST_TIMEOUT
from .ConnectionService import ConnectionService
class QueryService:
def run_query(self, cluster_name, index_name, query_json):
connection = ConnectionService().get_connection(cluster_name)
if connection.version.startswith("2"):
from elas... | 0.395018 | 0.111096 |
import httplib2
import os
import oauth2client
from oauth2client import client, tools, file
import base64
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from apiclient import errors, discovery
import mimetypes
from email.mime.image import MIMEImage
from email.mime.audio import MIMEAu... | backend/AXIOME3_app/email/gmail.py | import httplib2
import os
import oauth2client
from oauth2client import client, tools, file
import base64
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from apiclient import errors, discovery
import mimetypes
from email.mime.image import MIMEImage
from email.mime.audio import MIMEAu... | 0.201813 | 0.041423 |
import logging
import asyncio
import collections
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.helpers import aiohttp_client
_LOGGER = logging.getLogger(__name__)
DOMAIN = 'grohe_sense'
CONF_REFRESH_TOKEN = '<PASSWORD>'
CONFIG_SCHEMA = vol.Schema(
{
D... | __init__.py | import logging
import asyncio
import collections
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.helpers import aiohttp_client
_LOGGER = logging.getLogger(__name__)
DOMAIN = 'grohe_sense'
CONF_REFRESH_TOKEN = '<PASSWORD>'
CONFIG_SCHEMA = vol.Schema(
{
D... | 0.446977 | 0.056731 |
from datetime import datetime
from os import path
import sys
import textwrap
collections_path = path.abspath(path.join(path.dirname(__file__), '..', '..', '..'))
sys.path.append(collections_path)
import cerberus_collections # noqa: E402
schema = {'a_dict': {'keyschema': {'type': 'integer'},
'va... | docs/source/includes/generate.py |
from datetime import datetime
from os import path
import sys
import textwrap
collections_path = path.abspath(path.join(path.dirname(__file__), '..', '..', '..'))
sys.path.append(collections_path)
import cerberus_collections # noqa: E402
schema = {'a_dict': {'keyschema': {'type': 'integer'},
'va... | 0.237487 | 0.124665 |
import pyvo as vo
import warnings
from astroquery.jplhorizons import Horizons
from regions import PixCoord, PolygonPixelRegion, CirclePixelRegion
from polygon import parse_s_region
warnings.simplefilter('ignore') # block out warnings
def convert_stcs_for_adql(stcs):
adql = "POLYGON('ICRS', "
for elem in stc... | mast_tap.py |
import pyvo as vo
import warnings
from astroquery.jplhorizons import Horizons
from regions import PixCoord, PolygonPixelRegion, CirclePixelRegion
from polygon import parse_s_region
warnings.simplefilter('ignore') # block out warnings
def convert_stcs_for_adql(stcs):
adql = "POLYGON('ICRS', "
for elem in stc... | 0.614047 | 0.355859 |
import datetime as dt
import numpy as np
import pandas as pd
try:
import talib
except:
print('PLEASE install TALIB to call these methods')
pass
try:
import QUANTAXIS as QA
from QUANTAXIS.QAUtil.QAParameter import ORDER_DIRECTION
from QUANTAXIS.QAData.QADataStruct import (
QA_DataStruct... | GolemQ/indices/talib_fractal.py | import datetime as dt
import numpy as np
import pandas as pd
try:
import talib
except:
print('PLEASE install TALIB to call these methods')
pass
try:
import QUANTAXIS as QA
from QUANTAXIS.QAUtil.QAParameter import ORDER_DIRECTION
from QUANTAXIS.QAData.QADataStruct import (
QA_DataStruct... | 0.270384 | 0.182007 |
import socket
import serial
import pyttsx
class CellphoneServer(object):
def __init__(self,bind_addr,bind_port):
self.engine = pyttsx.init()
self.engine.setProperty('voice', 'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_ZIRA_11.0')
#ser = serial.Serial('COM3', 9600, timeout=0)
... | ServerCellphone.py | import socket
import serial
import pyttsx
class CellphoneServer(object):
def __init__(self,bind_addr,bind_port):
self.engine = pyttsx.init()
self.engine.setProperty('voice', 'HKEY_LOCAL_MACHINE\SOFTWARE\Microsoft\Speech\Voices\Tokens\TTS_MS_EN-US_ZIRA_11.0')
#ser = serial.Serial('COM3', 9600, timeout=0)
... | 0.244093 | 0.039158 |
from scripts.helpful_scripts import get_account, get_event_value
from scripts.modify_metadata import modify_metadata
from scripts.set_token_uri import set_token_uri
from scripts.deploy_nft import deploy_nft
from scripts.safe_mint import safe_mint
from nft_config import (
AMOUNT_TO_MINT,
COLLECTION_NAME,
COL... | tests/integration/test_integration_main.py | from scripts.helpful_scripts import get_account, get_event_value
from scripts.modify_metadata import modify_metadata
from scripts.set_token_uri import set_token_uri
from scripts.deploy_nft import deploy_nft
from scripts.safe_mint import safe_mint
from nft_config import (
AMOUNT_TO_MINT,
COLLECTION_NAME,
COL... | 0.488283 | 0.152694 |
import unittest
from wikisync.plugin import WikiSyncEnvironment
from wikisync.model import WikiSyncDao
from trac.test import EnvironmentStub
from pkg_resources import resource_filename
class WikiSyncModelTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub()
self.plugin = WikiS... | wikisync/tests/model.py | import unittest
from wikisync.plugin import WikiSyncEnvironment
from wikisync.model import WikiSyncDao
from trac.test import EnvironmentStub
from pkg_resources import resource_filename
class WikiSyncModelTestCase(unittest.TestCase):
def setUp(self):
self.env = EnvironmentStub()
self.plugin = WikiS... | 0.378 | 0.289246 |
import sys
sys.path.append("..")
import os
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import functools
import matplotlib.pyplot as plt
import tensorflow as tf
from dqn import molecules
from dqn import deep_q_networks
from dqn.py.SA_Score import sascorer
from chemutil import similarity
... | examples/generation/docking_generation/moldqn/chemgraph/docking_eval.py | import sys
sys.path.append("..")
import os
import json
import numpy as np
import pandas as pd
from tqdm import tqdm
import functools
import matplotlib.pyplot as plt
import tensorflow as tf
from dqn import molecules
from dqn import deep_q_networks
from dqn.py.SA_Score import sascorer
from chemutil import similarity
... | 0.21215 | 0.115886 |
import os
import sys
import time
import hikari
import tanjun
from bot import constants
component = tanjun.Component()
@component.with_slash_command
@tanjun.as_slash_command("status", "Get the status of the bot.")
async def command_status(
ctx: tanjun.abc.SlashContext,
bot: hikari.GatewayBot = tanjun.injec... | bot/modules/status.py |
import os
import sys
import time
import hikari
import tanjun
from bot import constants
component = tanjun.Component()
@component.with_slash_command
@tanjun.as_slash_command("status", "Get the status of the bot.")
async def command_status(
ctx: tanjun.abc.SlashContext,
bot: hikari.GatewayBot = tanjun.injec... | 0.602296 | 0.235812 |
"""Tests for the Hough transform."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from moonlight.vision import hough
class HoughTest(tf.test.TestCase):
def testHorizontalLines(self):
image = np.asarray... | moonlight/vision/hough_test.py | """Tests for the Hough transform."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from moonlight.vision import hough
class HoughTest(tf.test.TestCase):
def testHorizontalLines(self):
image = np.asarray... | 0.832543 | 0.74895 |
import math
import torch, torchvision
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes, num_decoder_layers=6, num_decoder_heads=8, uncertainty_gate_type="entropy", uncertainty_threshold=0, weighted_prediction=False, extended_output=False, gpu_streams=T... | core/model.py | import math
import torch, torchvision
import torch.nn as nn
import torch.nn.functional as F
class Model(nn.Module):
def __init__(self, num_classes, num_decoder_layers=6, num_decoder_heads=8, uncertainty_gate_type="entropy", uncertainty_threshold=0, weighted_prediction=False, extended_output=False, gpu_streams=T... | 0.920003 | 0.630372 |
import unittest
import os
from metis.File import File
from metis.Task import Task
from metis.Constants import Constants
class TaskTest(unittest.TestCase):
def test_task_initialized(self):
t1 = Task()
self.assertEqual(t1.initialized(), True)
def test_task_clone(self):
t1 = Task(a=1,b... | test/Task_t.py | import unittest
import os
from metis.File import File
from metis.Task import Task
from metis.Constants import Constants
class TaskTest(unittest.TestCase):
def test_task_initialized(self):
t1 = Task()
self.assertEqual(t1.initialized(), True)
def test_task_clone(self):
t1 = Task(a=1,b... | 0.408277 | 0.575648 |
import os
import sys
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# Add server root to path so that imports work properly regardless
# where script is called from.
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if root_path not in sys.path:
sys.path.append(roo... | server/scripts/orbit_vis.py | import os
import sys
import matplotlib.pyplot as plt
import matplotlib.gridspec as gridspec
# Add server root to path so that imports work properly regardless
# where script is called from.
root_path = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
if root_path not in sys.path:
sys.path.append(roo... | 0.475362 | 0.515803 |
from os import listdir
from os.path import isfile, join
import argparse
import re
import string
from PIL import Image, ImageFont, ImageDraw
import pystache
def print_hex(val):
return "{0:#0{1}x}".format(val,4)
def process_char(font, char):
# Calculate char info
char_size = font.getsize(char);
char_mask = font.... | scripts/build-font.py |
from os import listdir
from os.path import isfile, join
import argparse
import re
import string
from PIL import Image, ImageFont, ImageDraw
import pystache
def print_hex(val):
return "{0:#0{1}x}".format(val,4)
def process_char(font, char):
# Calculate char info
char_size = font.getsize(char);
char_mask = font.... | 0.195594 | 0.184988 |
import os
import threading
from base64 import b64decode
import asyncio
import boto3
import discord
import requests
token = os.environ.get("DISCORD_TOKEN", None)
CHANNEL_NAME = "announcements"
class DClient(discord.Client):
async def on_ready(self):
print("Logged in as {}".format(self.user.name))
class... | discord_client.py | import os
import threading
from base64 import b64decode
import asyncio
import boto3
import discord
import requests
token = os.environ.get("DISCORD_TOKEN", None)
CHANNEL_NAME = "announcements"
class DClient(discord.Client):
async def on_ready(self):
print("Logged in as {}".format(self.user.name))
class... | 0.370339 | 0.053403 |
from ctypes import *
from win32api import *
import psutil
import win32security
"""WinApi methods"""
K32 = windll.kernel32
ReadProcessMemory = K32.ReadProcessMemory
WriteProcessMemory = K32.WriteProcessMemory
OpenProcess = K32.OpenProcess
GetLastError = K32.GetLastError
"""All access"""
PAA = 0x1F0FFF
class ProcessE... | ProcessHandler.py |
from ctypes import *
from win32api import *
import psutil
import win32security
"""WinApi methods"""
K32 = windll.kernel32
ReadProcessMemory = K32.ReadProcessMemory
WriteProcessMemory = K32.WriteProcessMemory
OpenProcess = K32.OpenProcess
GetLastError = K32.GetLastError
"""All access"""
PAA = 0x1F0FFF
class ProcessE... | 0.378 | 0.103115 |
from eliot.testing import (
assertHasMessage, capture_logging
)
from eliot import Field, MessageType
from pyrsistent import PClass, field, InvariantException
from testtools.matchers import Is, Equals, Raises, MatchesException
from zope.interface import Interface, implementer
from .. import (
interface_decora... | flocker/common/test/test_interface.py | from eliot.testing import (
assertHasMessage, capture_logging
)
from eliot import Field, MessageType
from pyrsistent import PClass, field, InvariantException
from testtools.matchers import Is, Equals, Raises, MatchesException
from zope.interface import Interface, implementer
from .. import (
interface_decora... | 0.86129 | 0.582877 |
import lasagne
import theano
import theano.tensor as t
from lasagne.layers import ConcatLayer, Conv2DLayer, DenseLayer, GlobalPoolLayer
from lasagne.layers import InputLayer, Pool2DLayer
from lasagne.layers.normalization import batch_norm
from lasagne.nonlinearities import softmax
new_pool_size = 3
new_pad = 1
def p... | scripts/ann_architectures/imagenet/refine_inceptionV3_lasagne.py | import lasagne
import theano
import theano.tensor as t
from lasagne.layers import ConcatLayer, Conv2DLayer, DenseLayer, GlobalPoolLayer
from lasagne.layers import InputLayer, Pool2DLayer
from lasagne.layers.normalization import batch_norm
from lasagne.nonlinearities import softmax
new_pool_size = 3
new_pad = 1
def p... | 0.897785 | 0.567158 |
import requests
import os
import json
from dateutil.parser import parse
from datetime import datetime, timedelta
from Crypto.PublicKey import RSA
from Crypto.Random import get_random_bytes
from Crypto.Cipher import AES, PKCS1_OAEP
# Begin Private File Paths
def _ACCOUNT_ID():
return '132591'
def __PRIVATE_PATH():... | PyProjects/LightSpeed_auth/Token.py | import requests
import os
import json
from dateutil.parser import parse
from datetime import datetime, timedelta
from Crypto.PublicKey import RSA
from Crypto.Random import get_random_bytes
from Crypto.Cipher import AES, PKCS1_OAEP
# Begin Private File Paths
def _ACCOUNT_ID():
return '132591'
def __PRIVATE_PATH():... | 0.270673 | 0.0643 |
from typing import Set
import re
def tokenize(text: str) -> Set[str]:
text = text.lower() # standardize
all_words = re.findall("[a-z0-9']+", text) # extract words
return set(all_words) # remove duplicates
assert tokenize("Data science is science") == {"data", "science", "is", "science"}
# define c... | python_da/dsfs/mycode/ch13.py | from typing import Set
import re
def tokenize(text: str) -> Set[str]:
text = text.lower() # standardize
all_words = re.findall("[a-z0-9']+", text) # extract words
return set(all_words) # remove duplicates
assert tokenize("Data science is science") == {"data", "science", "is", "science"}
# define c... | 0.760339 | 0.414662 |
import os
import subprocess
import time
class File(object):
def __init__(self, filename):
self.name, self.extension = os.path.splitext(filename)
self.filename = filename
def open(self, flag):
return open(self.filename, flag)
def remove(self):
try:
os.remove(se... | fast/files.py | import os
import subprocess
import time
class File(object):
def __init__(self, filename):
self.name, self.extension = os.path.splitext(filename)
self.filename = filename
def open(self, flag):
return open(self.filename, flag)
def remove(self):
try:
os.remove(se... | 0.459804 | 0.096706 |
class InvalidResourceError(Exception):
"""Raised when a resource does not match the spec."""
pass
class InvalidSpecError(Exception):
"""Raised when a resource spec is invalid."""
pass
class ResourceSpec(dict):
"""A syntax specification for a class of valid resource IDs.
A resource ID is a path-like str... | service/data_store/resource_id.py | class InvalidResourceError(Exception):
"""Raised when a resource does not match the spec."""
pass
class InvalidSpecError(Exception):
"""Raised when a resource spec is invalid."""
pass
class ResourceSpec(dict):
"""A syntax specification for a class of valid resource IDs.
A resource ID is a path-like str... | 0.885607 | 0.335623 |
import keras.backend as K
class Doi(object):
'''
Interface for a distribution of interest.
The *distribution of interest* lets us specify the set of instances over
which we want our explanations to be faithful.
'''
def __call__(self, z):
'''
Takes a tensor, z, which is the i... | attribution/distributions.py | import keras.backend as K
class Doi(object):
'''
Interface for a distribution of interest.
The *distribution of interest* lets us specify the set of instances over
which we want our explanations to be faithful.
'''
def __call__(self, z):
'''
Takes a tensor, z, which is the i... | 0.920754 | 0.604428 |
import torch as th
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class QTran(nn.Module):
def __init__(self, args):
super(QTran, self).__init__()
self.args = args
self.n_agents = args.n_agents
self.n_actions = args.n_actions
self.state_dim = int(... | MAVEN/maven_code/src/modules/mixers/qtran.py | import torch as th
import torch.nn as nn
import torch.nn.functional as F
import numpy as np
class QTran(nn.Module):
def __init__(self, args):
super(QTran, self).__init__()
self.args = args
self.n_agents = args.n_agents
self.n_actions = args.n_actions
self.state_dim = int(... | 0.929728 | 0.539287 |
import matplotlib
from data.imsitu_loader import ImSitu, CudaDataLoader
from config import ModelConfig
import torch
from lib.misc import get_ranking
import numpy as np
from lib.imsitu_model import ImsituModel
import pandas as pd
from tqdm import tqdm
from lib.attribute_loss import AttributeLoss
from copy import deepcop... | figures/viz_activations.py | import matplotlib
from data.imsitu_loader import ImSitu, CudaDataLoader
from config import ModelConfig
import torch
from lib.misc import get_ranking
import numpy as np
from lib.imsitu_model import ImsituModel
import pandas as pd
from tqdm import tqdm
from lib.attribute_loss import AttributeLoss
from copy import deepcop... | 0.549641 | 0.303493 |
from __future__ import annotations
from typing import Text, List, Tuple, Dict
from nltk import wordpunct_tokenize, word_tokenize
from liyi_cute.utils.common import doubleQuotes
class WordTokenizerFast():
def __init__(self, *args, **kwargs):
self.do_lower_case = kwargs.pop("do_lower_case", True)
... | liyi_cute/processor/tokenizers/nltk_tokenizer.py |
from __future__ import annotations
from typing import Text, List, Tuple, Dict
from nltk import wordpunct_tokenize, word_tokenize
from liyi_cute.utils.common import doubleQuotes
class WordTokenizerFast():
def __init__(self, *args, **kwargs):
self.do_lower_case = kwargs.pop("do_lower_case", True)
... | 0.696784 | 0.240451 |
from __future__ import unicode_literals
import django
from django.conf import settings
from django.contrib.auth import get_user_model # noqa
try:
from django.urls import NoReverseMatch, reverse, Resolver404, get_script_prefix # noqa
except ImportError: # 1.8 backwards compat
from django.core.urlresolvers i... | tastypie/compat.py | from __future__ import unicode_literals
import django
from django.conf import settings
from django.contrib.auth import get_user_model # noqa
try:
from django.urls import NoReverseMatch, reverse, Resolver404, get_script_prefix # noqa
except ImportError: # 1.8 backwards compat
from django.core.urlresolvers i... | 0.394551 | 0.063919 |
from unittest import TestCase
from pythonzimbra.request_json import RequestJson
from pythonzimbra.request_xml import RequestXml
from pythonzimbra.tools.auth import authenticate
from pythonzimbra.communication import Communication
from tests import get_config
class TestAutoresponse(TestCase):
def test_autorespons... | tests/test_autoresponse.py | from unittest import TestCase
from pythonzimbra.request_json import RequestJson
from pythonzimbra.request_xml import RequestXml
from pythonzimbra.tools.auth import authenticate
from pythonzimbra.communication import Communication
from tests import get_config
class TestAutoresponse(TestCase):
def test_autorespons... | 0.770162 | 0.294177 |
import unittest
import numpy as np
import lightly
from lightly.active_learning.scorers import ScorerSemanticSegmentation
from lightly.active_learning.scorers import ScorerClassification
class TestScorerSemanticSegmentation(unittest.TestCase):
def setUp(self):
self.N = 100
self.W, self.H, self.C... | tests/active_learning/test_ScorerSemanticSegmentation.py | import unittest
import numpy as np
import lightly
from lightly.active_learning.scorers import ScorerSemanticSegmentation
from lightly.active_learning.scorers import ScorerClassification
class TestScorerSemanticSegmentation(unittest.TestCase):
def setUp(self):
self.N = 100
self.W, self.H, self.C... | 0.613931 | 0.594845 |
import os
from os.path import basename
from os.path import join
import subprocess
import sys
import cv2
import config
import utils
from pdf_to_jpg import PDF_TO_JPG_DIR
NEW_JPG_DIR = 'devided_jpgs'
CLEAN_PDF_DIR = 'clean_pdf'
CLEAN_PDF_SUFFIX = '_clean'
def is_color_img(img):
h, w = img.shape[:2]
# 画像の画素値(... | src/jpg_to_clean_pdf.py | import os
from os.path import basename
from os.path import join
import subprocess
import sys
import cv2
import config
import utils
from pdf_to_jpg import PDF_TO_JPG_DIR
NEW_JPG_DIR = 'devided_jpgs'
CLEAN_PDF_DIR = 'clean_pdf'
CLEAN_PDF_SUFFIX = '_clean'
def is_color_img(img):
h, w = img.shape[:2]
# 画像の画素値(... | 0.132683 | 0.207897 |
import random, pygame
pygame.init() #Gets all pygame libraries
whiteSquare = pygame.image.load('Snake Piece.png')
class Snake(object): #The entire Snake. Basically a doubly - linked list
def __init__(self, xRange, yRange):
self.tail = self.head = SnakePiece(int(int(xRange/10)/2)*10, int(int(... | SnakeObjects.py | import random, pygame
pygame.init() #Gets all pygame libraries
whiteSquare = pygame.image.load('Snake Piece.png')
class Snake(object): #The entire Snake. Basically a doubly - linked list
def __init__(self, xRange, yRange):
self.tail = self.head = SnakePiece(int(int(xRange/10)/2)*10, int(int(... | 0.430866 | 0.240931 |
from tempfile import gettempdir
from time import time
from pathlib import Path
from mock import patch
import pytest
from bets.model.match import Match
from bets.program_io.matches_output import (
fmt_to_csv,
fmt_to_table,
MatchesOutput
)
MATCHES = [
Match("Man UTD. - Arsenal", 2.78, 3.45, 2.90),
... | tests/program_io/test_matches_output.py | from tempfile import gettempdir
from time import time
from pathlib import Path
from mock import patch
import pytest
from bets.model.match import Match
from bets.program_io.matches_output import (
fmt_to_csv,
fmt_to_table,
MatchesOutput
)
MATCHES = [
Match("Man UTD. - Arsenal", 2.78, 3.45, 2.90),
... | 0.356447 | 0.424114 |
from django.forms.utils import flatatt
from django.forms.widgets import Widget, Textarea
from django.template import loader
from django.utils.safestring import mark_safe
from django.conf import settings
class ImageWidget(Widget):
template_name = 'widgets/image.html'
def get_context(self, name, value, attrs=N... | cruds_adminlte/widgets.py | from django.forms.utils import flatatt
from django.forms.widgets import Widget, Textarea
from django.template import loader
from django.utils.safestring import mark_safe
from django.conf import settings
class ImageWidget(Widget):
template_name = 'widgets/image.html'
def get_context(self, name, value, attrs=N... | 0.385837 | 0.103341 |
import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.utils.data.distributed
class Generator(nn.Module):
def __init__(self,
z_dim,
nlabels=1,
size=256,
embe... | DeepEBM/Torture/Models/GAN/resnet_imagenet.py | import torch
from torch import nn
from torch.nn import functional as F
from torch.autograd import Variable
import torch.utils.data
import torch.utils.data.distributed
class Generator(nn.Module):
def __init__(self,
z_dim,
nlabels=1,
size=256,
embe... | 0.936757 | 0.334019 |
import pylab
import numpy
from matplotlib import pyplot
def plotPhaseSpace( b, aTheta, aOmega, t, power, k):
pylab.clf()
pylab.cla()
label = str(b)
pylab.subplot(221)
pylab.plot( aTheta, aOmega, color="m", lw = 2)
pylab.xlabel(r"$\theta$ (radians) ", fontsize=10)
pylab.ylabel('$\om... | homework3_linear_algebra_FFT/time_series_analysis/plotter.py | import pylab
import numpy
from matplotlib import pyplot
def plotPhaseSpace( b, aTheta, aOmega, t, power, k):
pylab.clf()
pylab.cla()
label = str(b)
pylab.subplot(221)
pylab.plot( aTheta, aOmega, color="m", lw = 2)
pylab.xlabel(r"$\theta$ (radians) ", fontsize=10)
pylab.ylabel('$\om... | 0.581897 | 0.531513 |
import collections
import seaborn as sns
import matplotlib
from sklearn import metrics
from src.utils import utils
from src.models import ML
matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import numpy as np
import operator
import pandas as pd
plt.ioff()
conver... | src/visualization/visualization.py | import collections
import seaborn as sns
import matplotlib
from sklearn import metrics
from src.utils import utils
from src.models import ML
matplotlib.use('Agg', warn=False)
import matplotlib.pyplot as plt
from matplotlib.pyplot import figure
import numpy as np
import operator
import pandas as pd
plt.ioff()
conver... | 0.596551 | 0.273969 |
import json
from typing import Any, cast
from unittest.mock import patch
from django.http import HttpRequest
from django.http.response import JsonResponse
from django.test.client import RequestFactory
from rest_framework import status
from posthog.api.test.test_capture import mocked_get_ingest_context_from_token
from... | posthog/api/test/test_utils.py | import json
from typing import Any, cast
from unittest.mock import patch
from django.http import HttpRequest
from django.http.response import JsonResponse
from django.test.client import RequestFactory
from rest_framework import status
from posthog.api.test.test_capture import mocked_get_ingest_context_from_token
from... | 0.657648 | 0.212702 |
from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader, DistributedSampler
from fltk.datasets.distributed.dataset import DistDataset
from fltk.util.data_sampler_utils import LimitLabelsSampler
from fltk.util.non_iid_sampler import PDegreeSampler
class NonIIDMNISTDa... | fltk/datasets/non_iid_dataset/non_iid_mnist.py | from torchvision import datasets
from torchvision import transforms
from torch.utils.data import DataLoader, DistributedSampler
from fltk.datasets.distributed.dataset import DistDataset
from fltk.util.data_sampler_utils import LimitLabelsSampler
from fltk.util.non_iid_sampler import PDegreeSampler
class NonIIDMNISTDa... | 0.87401 | 0.55646 |
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import csv as csv
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_validation import train_test_split
from math import *
from datetime import datetime
import sklearn.cluster... | 20171126-258-assignment2/code/ref/code/pca.py | import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import csv as csv
from sklearn.ensemble import GradientBoostingRegressor
from sklearn.ensemble import RandomForestRegressor
from sklearn.cross_validation import train_test_split
from math import *
from datetime import datetime
import sklearn.cluster... | 0.319227 | 0.504944 |
import torch
import torch.nn as nn
import numpy as np
import tqdm
from lib.options import BaseOption
class DGM_Layer(nn.Module):
def __init__(self, dim_x, dim_S, activation='Tanh'):
super(DGM_Layer, self).__init__()
if activation == 'ReLU':
self.activation = nn.ReLU()
... | lib/dgm.py | import torch
import torch.nn as nn
import numpy as np
import tqdm
from lib.options import BaseOption
class DGM_Layer(nn.Module):
def __init__(self, dim_x, dim_S, activation='Tanh'):
super(DGM_Layer, self).__init__()
if activation == 'ReLU':
self.activation = nn.ReLU()
... | 0.940147 | 0.489198 |
from naoqi import ALProxy
class ALMotion(object):
def __init__(self, session):
self.proxy = None
self.session = session
def force_connect(self):
self.proxy = self.session.service("ALMotion")
def angleInterpolation(self, names, angleLists, timeLists, isAbsolute):
"""Int... | naoqi_proxy_python_classes/ALMotion.py |
from naoqi import ALProxy
class ALMotion(object):
def __init__(self, session):
self.proxy = None
self.session = session
def force_connect(self):
self.proxy = self.session.service("ALMotion")
def angleInterpolation(self, names, angleLists, timeLists, isAbsolute):
"""Int... | 0.836721 | 0.59072 |
import os
import sys
import unittest
from collections import OrderedDict
import mock
import unittest2
from cnpsdk import (utils, chargeback_update)
package_root = os.path.dirname(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
sys.path.insert(0, package_root)
conf = utils.Configuration()
class TestCh... | tests/unit/test_chargeback_update.py |
import os
import sys
import unittest
from collections import OrderedDict
import mock
import unittest2
from cnpsdk import (utils, chargeback_update)
package_root = os.path.dirname(os.path.dirname(os.path.abspath(os.path.dirname(__file__))))
sys.path.insert(0, package_root)
conf = utils.Configuration()
class TestCh... | 0.36727 | 0.187226 |
import logging
from collections import namedtuple
from autotrail.core.dag import Step, topological_traverse
from autotrail.core.socket_communication import HandlerResult
class APICallName:
"""Namepsace for the API call names. Use this class instead of plain strings."""
# List of valid API call names
# T... | autotrail/layer1/api.py | import logging
from collections import namedtuple
from autotrail.core.dag import Step, topological_traverse
from autotrail.core.socket_communication import HandlerResult
class APICallName:
"""Namepsace for the API call names. Use this class instead of plain strings."""
# List of valid API call names
# T... | 0.82566 | 0.396535 |
import os
import sys
import argparse
from .context import setup_context, gather_tags
from .exceptions import YaggyError
from .logging import setup_logging, get_logger
from .parser import run_parser
from .ssh import disconnect
from .templates import render
from .utils import pick, mutate
def tags_type(value):
re... | src/yaggy/cli.py |
import os
import sys
import argparse
from .context import setup_context, gather_tags
from .exceptions import YaggyError
from .logging import setup_logging, get_logger
from .parser import run_parser
from .ssh import disconnect
from .templates import render
from .utils import pick, mutate
def tags_type(value):
re... | 0.329068 | 0.106877 |
from typing import Generator
from abc import ABC, abstractmethod
class BaseScraper(ABC):
"""Scraper Abstract Base Class."""
@abstractmethod
def scrape(self, chunksize: int, **kwargs) -> Generator:
"""
Download any new information not yet downloaded.
The first time this method is ... | geniepy/src/geniepy/datamgmt/scrapers.py | from typing import Generator
from abc import ABC, abstractmethod
class BaseScraper(ABC):
"""Scraper Abstract Base Class."""
@abstractmethod
def scrape(self, chunksize: int, **kwargs) -> Generator:
"""
Download any new information not yet downloaded.
The first time this method is ... | 0.897844 | 0.597579 |
import statistics
from typing import Iterable, Collection, Union, ValuesView
def get_set_by_setting_name(name: str, measurements: Iterable) -> set:
result = set()
for m in measurements:
result.add(m.settings.dict[name])
return result
def get_summary_by_set(my_set: set) -> str:
result = ""
... | pi88reader/utils_pi88measurements.py | import statistics
from typing import Iterable, Collection, Union, ValuesView
def get_set_by_setting_name(name: str, measurements: Iterable) -> set:
result = set()
for m in measurements:
result.add(m.settings.dict[name])
return result
def get_summary_by_set(my_set: set) -> str:
result = ""
... | 0.53777 | 0.320542 |
import os
# Turn off some memory warning by changing warning level before loading module
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
from tensorflow... | main.py | import os
# Turn off some memory warning by changing warning level before loading module
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
import tensorflow as tf
import numpy as np
from tensorflow.keras import layers
from tensorflow.keras.layers import Dense, Flatten, Conv2D
from tensorflow.keras import Model
from tensorflow... | 0.696578 | 0.362574 |
from flask import Flask, render_template, session, url_for, request, redirect, escape
import os
app = Flask(__name__)
app.config['SECRET_KEY'] = 'leyni'
vorur = [ [0,"Arnar","arnar.jpg","1000000"],
[1,"Guðjón","gudjon.jpg","700000"],
[2,"Ágúst","agust.jpg","850000"],
[3,"Lorraine... | app.py | from flask import Flask, render_template, session, url_for, request, redirect, escape
import os
app = Flask(__name__)
app.config['SECRET_KEY'] = 'leyni'
vorur = [ [0,"Arnar","arnar.jpg","1000000"],
[1,"Guðjón","gudjon.jpg","700000"],
[2,"Ágúst","agust.jpg","850000"],
[3,"Lorraine... | 0.253399 | 0.074198 |
from pathlib import Path
import pytest
from puzzle.__main__ import (
_random_words,
_validate_puzzle_grid_size,
_validate_puzzle_word,
_validate_puzzle_words_path,
)
pytestmark = pytest.mark.unittest
_test_path: Path = Path(__file__).parent / 'words.txt'
@pytest.mark.parametrize(
'word, grid_si... | tests/test_tool_payload.py | from pathlib import Path
import pytest
from puzzle.__main__ import (
_random_words,
_validate_puzzle_grid_size,
_validate_puzzle_word,
_validate_puzzle_words_path,
)
pytestmark = pytest.mark.unittest
_test_path: Path = Path(__file__).parent / 'words.txt'
@pytest.mark.parametrize(
'word, grid_si... | 0.857932 | 0.413803 |
import json
## Session
def RealTimeResponseBatchInitSession(self, ids, timeout=30, timeout_duration="30s", batch=None):
'''/real-time-response/combined/batch-init-session/v1'''
body = {
"host_ids": ids if type(ids) == list else [ids],
"queue_offline": True
}
if batch:
body["e... | crowdstrikeclient/_CrowdstrikeRTR.py | import json
## Session
def RealTimeResponseBatchInitSession(self, ids, timeout=30, timeout_duration="30s", batch=None):
'''/real-time-response/combined/batch-init-session/v1'''
body = {
"host_ids": ids if type(ids) == list else [ids],
"queue_offline": True
}
if batch:
body["e... | 0.31216 | 0.081483 |
import asyncio
import argparse
import json
import logging
from kin import KinClient, Environment, Keypair
from kin.blockchain.builder import Builder
from helpers import (NETWORK_NAME, MIN_FEE,
load_accounts, generate_keypairs, get_sequences_multiple_endpoints,
create_accounts... | cmd/python/create.py | import asyncio
import argparse
import json
import logging
from kin import KinClient, Environment, Keypair
from kin.blockchain.builder import Builder
from helpers import (NETWORK_NAME, MIN_FEE,
load_accounts, generate_keypairs, get_sequences_multiple_endpoints,
create_accounts... | 0.613815 | 0.122313 |
from typing import Any
from uuid import UUID
from .....typing import UserID
from ....ticketing.dbmodels.ticket_bundle import TicketBundle
from ....ticketing import (
category_service as ticket_category_service,
ticket_bundle_service,
)
from ....ticketing.transfer.models import TicketBundleID, TicketCategoryID... | byceps/services/shop/order/actions/ticket_bundle.py | from typing import Any
from uuid import UUID
from .....typing import UserID
from ....ticketing.dbmodels.ticket_bundle import TicketBundle
from ....ticketing import (
category_service as ticket_category_service,
ticket_bundle_service,
)
from ....ticketing.transfer.models import TicketBundleID, TicketCategoryID... | 0.633524 | 0.173183 |
import tensorflow as tf
# tfjs-converter cannot convert tf.linspace due to broadcasting issues
def _linspace(start, stop, num, dtype=tf.float32):
range = tf.range(0, num, dtype=dtype)
start = tf.cast(start, dtype)
stop = tf.cast(stop, dtype)
num = tf.cast(num, dtype)
return start + (stop - start) ... | lidbox/features/mel_ops.py | import tensorflow as tf
# tfjs-converter cannot convert tf.linspace due to broadcasting issues
def _linspace(start, stop, num, dtype=tf.float32):
range = tf.range(0, num, dtype=dtype)
start = tf.cast(start, dtype)
stop = tf.cast(stop, dtype)
num = tf.cast(num, dtype)
return start + (stop - start) ... | 0.897105 | 0.364891 |
from __future__ import absolute_import, unicode_literals
from django.conf import settings as django_settings
from django.utils import translation
import elasticsearch
from elasticsearch import NotFoundError, ImproperlyConfigured
import haystack
from haystack.backends import BaseEngine
from haystack.backends.elasticsear... | multilingual/elasticsearch_backend.py | from __future__ import absolute_import, unicode_literals
from django.conf import settings as django_settings
from django.utils import translation
import elasticsearch
from elasticsearch import NotFoundError, ImproperlyConfigured
import haystack
from haystack.backends import BaseEngine
from haystack.backends.elasticsear... | 0.695648 | 0.101991 |
import logging
import threading
import argparse
from queue import Queue
from collections import deque
import fakesmartrowble
import fakerower
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logHandler = logging.StreamHandler()
filelogHandler = logging.FileHandler("logs.log")
formatter = logging.... | src/testing/fakesmartrowthreads.py | import logging
import threading
import argparse
from queue import Queue
from collections import deque
import fakesmartrowble
import fakerower
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
logHandler = logging.StreamHandler()
filelogHandler = logging.FileHandler("logs.log")
formatter = logging.... | 0.098101 | 0.050682 |
class Category:
ledger: list[dict]
total: float
name: str
def __init__(self, name):
self.name = name
self.ledger = []
self.total = 0.0
def __str__(self):
result = ''
result += self.name.center(30, '*') + '\n'
for item in self.ledger:
a, m... | Budget-app/budget.py | class Category:
ledger: list[dict]
total: float
name: str
def __init__(self, name):
self.name = name
self.ledger = []
self.total = 0.0
def __str__(self):
result = ''
result += self.name.center(30, '*') + '\n'
for item in self.ledger:
a, m... | 0.489748 | 0.17037 |
from __future__ import division
from os.path import dirname, join
from datetime import datetime
from pyNN.nineml.read import Network
from pyNN.utility import SimulationProgressBar
import nineml.user as nineml
from nineml.units import ms, mV, nA, unitless, Hz, Mohm
from utility import psp_height
#CATALOG_URL = "/home/d... | python/simple_network/simple_network_nineml.py | from __future__ import division
from os.path import dirname, join
from datetime import datetime
from pyNN.nineml.read import Network
from pyNN.utility import SimulationProgressBar
import nineml.user as nineml
from nineml.units import ms, mV, nA, unitless, Hz, Mohm
from utility import psp_height
#CATALOG_URL = "/home/d... | 0.487063 | 0.259878 |
import os
import argparse
import tensorflow as tf
import pdb
from scripts import configs_cv as C
from rl.configs import parser as ps
from rl.tools.utils import tf_utils as U
def setup_pdb(mode):
if mode == 'on':
pdb.set_trace_force = pdb.set_trace
return
elif mode == 'off':
pdb.set_tr... | scripts/rl_exp_cv_var.py | import os
import argparse
import tensorflow as tf
import pdb
from scripts import configs_cv as C
from rl.configs import parser as ps
from rl.tools.utils import tf_utils as U
def setup_pdb(mode):
if mode == 'on':
pdb.set_trace_force = pdb.set_trace
return
elif mode == 'off':
pdb.set_tr... | 0.215598 | 0.095687 |
from biocrnpyler.chemical_reaction_network import Species, Reaction, ComplexSpecies, ChemicalReactionNetwork
print("Start")
#Names of different supported propensities
propensity_types = ['hillpositive', 'proportionalhillpositive', 'hillnegative', 'proportionalhillnegative', 'massaction', 'general']
kb = 100
ku = 10
... | examples/Old Examples and Tests/Testing Propensities.py | from biocrnpyler.chemical_reaction_network import Species, Reaction, ComplexSpecies, ChemicalReactionNetwork
print("Start")
#Names of different supported propensities
propensity_types = ['hillpositive', 'proportionalhillpositive', 'hillnegative', 'proportionalhillnegative', 'massaction', 'general']
kb = 100
ku = 10
... | 0.277669 | 0.484075 |
from gpxpy import *
import unittest
import StringIO
import math
import gpxpy.tests
import datetime
class ParseTest(unittest.TestCase):
def test_parse_track1(self):
gpx = gpxpy.tests.load('track-1.gpx')
# parsed correctly - 1 track
self.assertEquals(len(gpx.tracks),1)
self.assertEquals(len(gpx.way... | gpxpy/tests/gpxpy_test.py |
from gpxpy import *
import unittest
import StringIO
import math
import gpxpy.tests
import datetime
class ParseTest(unittest.TestCase):
def test_parse_track1(self):
gpx = gpxpy.tests.load('track-1.gpx')
# parsed correctly - 1 track
self.assertEquals(len(gpx.tracks),1)
self.assertEquals(len(gpx.way... | 0.48438 | 0.550245 |
import os
import argparse
import numpy as np
import tensorflow as tf
import smdebug.tensorflow as smd
from model_def import get_resnet50
hook = smd.KerasHook.create_from_json_file()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def parse_args():
parser = argparse.ArgumentParser()
# hyperparameters are passed a... | tf-2-data-parallelism/src/train_resnet_simple.py | import os
import argparse
import numpy as np
import tensorflow as tf
import smdebug.tensorflow as smd
from model_def import get_resnet50
hook = smd.KerasHook.create_from_json_file()
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
def parse_args():
parser = argparse.ArgumentParser()
# hyperparameters are passed a... | 0.707809 | 0.213521 |
__all__ = ['img2tensor', 'leaf_modules', 'gram_matrix', 'get_in_channels', 'get_out_channels', 'reflection_conv',
'conv_layer', 'res_block', 'Halve', 'Double', 'get_encoder', 'FeatureLoss', 'AutoEncoder']
# Cell
from functools import partial
from pathlib import Path
import matplotlib.pyplot as plt
import ... | style_transfer/models.py |
__all__ = ['img2tensor', 'leaf_modules', 'gram_matrix', 'get_in_channels', 'get_out_channels', 'reflection_conv',
'conv_layer', 'res_block', 'Halve', 'Double', 'get_encoder', 'FeatureLoss', 'AutoEncoder']
# Cell
from functools import partial
from pathlib import Path
import matplotlib.pyplot as plt
import ... | 0.810779 | 0.351951 |
import mimetypes
from pathlib import Path
from PIL import Image
class ResizeImage:
"""
Redimensiona una imagen indicando la anchura y la altura.
"""
def __init__(self, file_in, file_out, args):
"""
Args:
file_in (Path): Archivo de entrada (imagen)
file_out (Pa... | reto-08/fpellicer/src/resize_image.py | import mimetypes
from pathlib import Path
from PIL import Image
class ResizeImage:
"""
Redimensiona una imagen indicando la anchura y la altura.
"""
def __init__(self, file_in, file_out, args):
"""
Args:
file_in (Path): Archivo de entrada (imagen)
file_out (Pa... | 0.624408 | 0.280561 |
# Copyright (c) 2021, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import logging
from ads.common.model_metadata import Framework
from ads.model.extractor.model_info_extractor import ModelInfoExtractor
class LightgbmExtra... | ads/model/extractor/lightgbm_extractor.py |
# Copyright (c) 2021, 2022 Oracle and/or its affiliates.
# Licensed under the Universal Permissive License v 1.0 as shown at https://oss.oracle.com/licenses/upl/
import logging
from ads.common.model_metadata import Framework
from ads.model.extractor.model_info_extractor import ModelInfoExtractor
class LightgbmExtra... | 0.935383 | 0.545407 |
from covsirphy.util.evaluator import Evaluator
from covsirphy.util.term import Term
from covsirphy.ode.mbase import ModelBase
from covsirphy.regression.param_elastic_net import _ParamElasticNetRegressor
from covsirphy.regression.rate_elastic_net import _RateElasticNetRegressor
class RegressionHandler(Term):
"""
... | covsirphy/regression/reg_handler.py |
from covsirphy.util.evaluator import Evaluator
from covsirphy.util.term import Term
from covsirphy.ode.mbase import ModelBase
from covsirphy.regression.param_elastic_net import _ParamElasticNetRegressor
from covsirphy.regression.rate_elastic_net import _RateElasticNetRegressor
class RegressionHandler(Term):
"""
... | 0.954932 | 0.688658 |
import tkinter as tk
from tkinter import ttk
"""
Display tkinter widgets in a resizable grid.
WidgetGrid.content is derived from tkinter.Text.
Its appearance can be configured with all the options of Text.
The tabs option controls the space between columns of widgets.
The spacing2 option controls the space between row... | widgetgrid.py | import tkinter as tk
from tkinter import ttk
"""
Display tkinter widgets in a resizable grid.
WidgetGrid.content is derived from tkinter.Text.
Its appearance can be configured with all the options of Text.
The tabs option controls the space between columns of widgets.
The spacing2 option controls the space between row... | 0.705379 | 0.281461 |
from user import User
from cred import Info
def create_account(name,e_mail):
'''
creating a new account
'''
new_user=User(name,e_mail)
return new_user
def create_credentials(facebk_pass,email_pass):
'''
create credentials of password and email
'''
new_cred=Info(facebk_pass,email_p... | run.py | from user import User
from cred import Info
def create_account(name,e_mail):
'''
creating a new account
'''
new_user=User(name,e_mail)
return new_user
def create_credentials(facebk_pass,email_pass):
'''
create credentials of password and email
'''
new_cred=Info(facebk_pass,email_p... | 0.164483 | 0.075007 |
# <markdowncell>
# #Generates Urdu versions of output
#
# This is a bit sloppy right now.
# <codecell>
import sys
sys.path.append('./graphparser/')
import graphparser
urdup = graphparser.GraphParser('./graphparser/settings/urdu.yaml')
nagarip = graphparser.GraphParser('./graphparser/settings/devanagari.yaml')
# ... | generate_urdu.py |
# <markdowncell>
# #Generates Urdu versions of output
#
# This is a bit sloppy right now.
# <codecell>
import sys
sys.path.append('./graphparser/')
import graphparser
urdup = graphparser.GraphParser('./graphparser/settings/urdu.yaml')
nagarip = graphparser.GraphParser('./graphparser/settings/devanagari.yaml')
# ... | 0.36139 | 0.123023 |
r"""
Set of utilities to open databases.
"""
from m4db_database.configuration import read_config_from_environ
from m4db_database import global_vars
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.pool import NullPool
from m4db_database.orm.latest import Base as BaseLates... | lib/m4db_database/sessions.py | r"""
Set of utilities to open databases.
"""
from m4db_database.configuration import read_config_from_environ
from m4db_database import global_vars
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from sqlalchemy.pool import NullPool
from m4db_database.orm.latest import Base as BaseLates... | 0.567697 | 0.161916 |
from datetime import timedelta
from django.apps import apps
from django.contrib.contenttypes.models import ContentType
from django.core.management import BaseCommand
from django.db import DEFAULT_DB_ALIAS, models, transaction
from django.utils.timezone import now
from polymorphic.models import PolymorphicModel
from .... | paper_uploads/management/commands/clean_uploads.py | from datetime import timedelta
from django.apps import apps
from django.contrib.contenttypes.models import ContentType
from django.core.management import BaseCommand
from django.db import DEFAULT_DB_ALIAS, models, transaction
from django.utils.timezone import now
from polymorphic.models import PolymorphicModel
from .... | 0.527317 | 0.209369 |
from pid import PID
from lowpass import LowPassFilter
from yaw_controller import YawController
import rospy
GAS_DENSITY = 2.858
ONE_MPH = 0.44704
class Controller(object):
def __init__(self, vehicle_mass, fuel_capacity, brake_deadband,
decel_limit, accel_limit, wheel_radius, wheel_base, steer_r... | ros/src/twist_controller/twist_controller.py | from pid import PID
from lowpass import LowPassFilter
from yaw_controller import YawController
import rospy
GAS_DENSITY = 2.858
ONE_MPH = 0.44704
class Controller(object):
def __init__(self, vehicle_mass, fuel_capacity, brake_deadband,
decel_limit, accel_limit, wheel_radius, wheel_base, steer_r... | 0.547222 | 0.165897 |