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 |
|---|---|---|---|---|
__updated__ = '2019-12-02'
__version_info__ = (19, 8, 0)
__version__ = '.'.join(map(str, __version_info__))
# Import system type stuff
# Import PyMh files
from Modules.Core.Utilities import extract_tools
from Modules.Core.Utilities.debug_tools import PrettyFormatAny
from Modules.Core import logging_pyh as Logger
... | Project/src/Modules/House/Hvac/hvac.py | __updated__ = '2019-12-02'
__version_info__ = (19, 8, 0)
__version__ = '.'.join(map(str, __version_info__))
# Import system type stuff
# Import PyMh files
from Modules.Core.Utilities import extract_tools
from Modules.Core.Utilities.debug_tools import PrettyFormatAny
from Modules.Core import logging_pyh as Logger
... | 0.422147 | 0.105441 |
from django.views.generic.simple import direct_to_template
from django.conf import settings
from django.http import HttpResponse, HttpResponseBadRequest
import flickrapi
import flickrapi.shorturl
import simplejson
import bueda
import logging
def demo(request):
flickr_conn = flickrapi.FlickrAPI(settings.FLICKR_AP... | bueda_flickr_mashup/views.py | from django.views.generic.simple import direct_to_template
from django.conf import settings
from django.http import HttpResponse, HttpResponseBadRequest
import flickrapi
import flickrapi.shorturl
import simplejson
import bueda
import logging
def demo(request):
flickr_conn = flickrapi.FlickrAPI(settings.FLICKR_AP... | 0.218419 | 0.051439 |
from custom_dataset import Multimodal_Data_Generator
from sentence_cnn import SentenceCNN
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.resnet import ResNet50
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau
from tensorflow.... | src/models/train_multimodal_model_2.py | from custom_dataset import Multimodal_Data_Generator
from sentence_cnn import SentenceCNN
from tensorflow.keras.applications.vgg16 import VGG16
from tensorflow.keras.applications.resnet import ResNet50
from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint, TensorBoard, ReduceLROnPlateau
from tensorflow.... | 0.798776 | 0.351673 |
import datetime
from django.test import TestCase
from django.utils import timezone
from .models import Article
from infinite_scroll_pagination.paginator import SeekPaginator
from infinite_scroll_pagination import paginator as inf_paginator
class Paginator2FieldsTest(TestCase):
def setUp(self):
date = ... | tests/test_multi_fields.py |
import datetime
from django.test import TestCase
from django.utils import timezone
from .models import Article
from infinite_scroll_pagination.paginator import SeekPaginator
from infinite_scroll_pagination import paginator as inf_paginator
class Paginator2FieldsTest(TestCase):
def setUp(self):
date = ... | 0.438545 | 0.383872 |
from __future__ import absolute_import, division, print_function, unicode_literals
from pkg_resources import resource_filename
import bioutils.sequences
import ometa.runtime
import parsley
import hgvs.edit
import hgvs.hgvsposition
import hgvs.location
import hgvs.posedit
import hgvs.variant
class Parser(object):
... | hgvs/parser.py | from __future__ import absolute_import, division, print_function, unicode_literals
from pkg_resources import resource_filename
import bioutils.sequences
import ometa.runtime
import parsley
import hgvs.edit
import hgvs.hgvsposition
import hgvs.location
import hgvs.posedit
import hgvs.variant
class Parser(object):
... | 0.725162 | 0.380327 |
from numpy import floor, meshgrid, multiply, divide, square, sqrt, pi, arange, log, pad, exp
from numpy.fft import fftshift, ifftshift, fft2, ifft2
def Paganin(image, fresnel, beta_delta, zero_compensation = 0.01):
"""
This function makes the phase retrieval with Paganin method.
Refer to the book: ... | maximus48/sidi_phare.py | from numpy import floor, meshgrid, multiply, divide, square, sqrt, pi, arange, log, pad, exp
from numpy.fft import fftshift, ifftshift, fft2, ifft2
def Paganin(image, fresnel, beta_delta, zero_compensation = 0.01):
"""
This function makes the phase retrieval with Paganin method.
Refer to the book: ... | 0.644225 | 0.574216 |
from typing import Dict
import os
from collections import OrderedDict
from argparse import ArgumentParser, Namespace
from multiprocessing import cpu_count
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightn... | src/sagemaker_defect_detection/classifier.py | from typing import Dict
import os
from collections import OrderedDict
from argparse import ArgumentParser, Namespace
from multiprocessing import cpu_count
import torch
import torch.optim as optim
import torch.nn.functional as F
from torch.utils.data import DataLoader
import pytorch_lightning as pl
from pytorch_lightn... | 0.924266 | 0.368008 |
import turtle
import scoreboard
import players
import balls
import time
GAME_SCREEN_WIDTH = 800
GAME_SCREEN_HEIGHT = 600
game_screen = turtle.Screen()
game_screen.setup(width=GAME_SCREEN_WIDTH, height=GAME_SCREEN_HEIGHT)
game_screen.bgcolor("black")
game_screen.title("PONG")
game_screen.tracer(0)
divider = turtle.Tu... | day_22/main.py | import turtle
import scoreboard
import players
import balls
import time
GAME_SCREEN_WIDTH = 800
GAME_SCREEN_HEIGHT = 600
game_screen = turtle.Screen()
game_screen.setup(width=GAME_SCREEN_WIDTH, height=GAME_SCREEN_HEIGHT)
game_screen.bgcolor("black")
game_screen.title("PONG")
game_screen.tracer(0)
divider = turtle.Tu... | 0.33231 | 0.072637 |
import os
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
import tensorflow as tf
import sklearn
from functools import partial
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer, Dense
from rdkit import Chem
from rdkit.Chem import AllChem
def ranks_to_acc(fo... | scripts/04_ml_fixed_model.py | import os
import numpy as np
import pandas as pd
from joblib import Parallel, delayed
import tensorflow as tf
import sklearn
from functools import partial
from tensorflow.keras import backend as K
from tensorflow.keras.layers import Layer, Dense
from rdkit import Chem
from rdkit.Chem import AllChem
def ranks_to_acc(fo... | 0.317426 | 0.193681 |
from typing import List, Tuple
import cv2
import io
import time
import imagehash
import threading
import cv2
from contextlib import contextmanager
from PIL import Image
from fluxhelper import Logger
from queue import Queue
VIDEO_MAPPINGS = {
"video/x-msvideo": ".avi",
"video/mp4": ".mp4",
"video/mpeg": ".... | fastnsfw/helpers.py | from typing import List, Tuple
import cv2
import io
import time
import imagehash
import threading
import cv2
from contextlib import contextmanager
from PIL import Image
from fluxhelper import Logger
from queue import Queue
VIDEO_MAPPINGS = {
"video/x-msvideo": ".avi",
"video/mp4": ".mp4",
"video/mpeg": ".... | 0.726329 | 0.212293 |
import argparse
import json
import logging
import os
import requests
import shutil
import subprocess
import tomputils.util as tutil
from datetime import datetime, timedelta
from pathlib import Path
from requests.auth import HTTPDigestAuth
from requests.exceptions import Timeout, ConnectionError, RequestException
from... | support/bin/1FPSVideo.py |
import argparse
import json
import logging
import os
import requests
import shutil
import subprocess
import tomputils.util as tutil
from datetime import datetime, timedelta
from pathlib import Path
from requests.auth import HTTPDigestAuth
from requests.exceptions import Timeout, ConnectionError, RequestException
from... | 0.207536 | 0.059674 |
import cv2
import numpy as np
import os
import json
import glob
frame_width, frame_height = 100, 100
dim = (frame_width,frame_height)
fps = 25
output_extension = '.avi'
def extract_hands(hands, bb, out, image):
buff = 10
confidence_threshold = .1
confidence = np.mean(hands[:, 2])
if confidence >= confidenc... | utils/crop_hand.py | import cv2
import numpy as np
import os
import json
import glob
frame_width, frame_height = 100, 100
dim = (frame_width,frame_height)
fps = 25
output_extension = '.avi'
def extract_hands(hands, bb, out, image):
buff = 10
confidence_threshold = .1
confidence = np.mean(hands[:, 2])
if confidence >= confidenc... | 0.183484 | 0.262021 |
from accounting import models
from django.db import transaction
from rest_framework.serializers import HyperlinkedModelSerializer
class LedgerSerializer(HyperlinkedModelSerializer):
class Meta:
model = models.Ledger
fields = (
'url',
'id',
'persons_company',
... | accounting/serializers.py | from accounting import models
from django.db import transaction
from rest_framework.serializers import HyperlinkedModelSerializer
class LedgerSerializer(HyperlinkedModelSerializer):
class Meta:
model = models.Ledger
fields = (
'url',
'id',
'persons_company',
... | 0.603465 | 0.118896 |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import models
from dataset import get_feature
def rescore_valid(cfg, temp, ori_scores):
temp = np.array(temp)
... | lib/core/rescore.py |
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import torch
import torch.nn as nn
from torch.autograd import Variable
import models
from dataset import get_feature
def rescore_valid(cfg, temp, ori_scores):
temp = np.array(temp)
... | 0.748168 | 0.210482 |
from django.urls import reverse
from simple.models import SimpleManager, SimpleModel
def test_AbstractIAMUser(django_user_model):
username = 'user'
user = django_user_model.objects.create_user(username=username)
assert user.full_name == ""
assert user.display_name == username
assert user.initials... | tests/contrib/test_users.py | from django.urls import reverse
from simple.models import SimpleManager, SimpleModel
def test_AbstractIAMUser(django_user_model):
username = 'user'
user = django_user_model.objects.create_user(username=username)
assert user.full_name == ""
assert user.display_name == username
assert user.initials... | 0.511961 | 0.280764 |
import unittest
from serviceparser import Parser
import yaml
import copy
import json
from groupinfo import GroupInfo
from registryinfo import RegistryInfo
class ServiceParserTests(unittest.TestCase):
def _get_default_parser(self, service_info=None):
group_info = GroupInfo('group_name', 'group_qualifier',... | src/tasks/dockerDeploy/acs-kubernetes/test_serviceparser.py | import unittest
from serviceparser import Parser
import yaml
import copy
import json
from groupinfo import GroupInfo
from registryinfo import RegistryInfo
class ServiceParserTests(unittest.TestCase):
def _get_default_parser(self, service_info=None):
group_info = GroupInfo('group_name', 'group_qualifier',... | 0.568775 | 0.192065 |
import os
import json
import argparse
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.optim as optimiser
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
from core.dataset import LungDataset
from core.model import PointNe... | lung-annotator/train.py | import os
import json
import argparse
import numpy as np
from sklearn.model_selection import train_test_split
import torch
import torch.optim as optimiser
import torch.nn.functional as F
from torch.utils.data import DataLoader
from tqdm import tqdm
from core.dataset import LungDataset
from core.model import PointNe... | 0.50952 | 0.304623 |
from cc3d.core.PySteppables import *
from collections import defaultdict
from collections import namedtuple
# For convenience we define a named tuple that will store anchor parameters
AnchorData = namedtuple('AnchorData', 'lambda_ target_length max_length x y z')
class FocalPointPlasticityAnchorSteppable(SteppableBa... | main/PluginDemos/FocalPointPlasticityAnchorsPython/Simulation/FocalPointPlasticityAnchorsSteppables.py | from cc3d.core.PySteppables import *
from collections import defaultdict
from collections import namedtuple
# For convenience we define a named tuple that will store anchor parameters
AnchorData = namedtuple('AnchorData', 'lambda_ target_length max_length x y z')
class FocalPointPlasticityAnchorSteppable(SteppableBa... | 0.677261 | 0.349366 |
import subprocess, sys, itertools, getpass
username = getpass.getuser()
usage_error = False
allservers = ["maris%03i" % i for i in range(2, 68)]
if len(sys.argv) == 2:
if sys.argv[1].lower() == "default":
servers = ["maris%03i" % i for i in range(25,33) + range(61,68)]
elif sys.argv[1].lower() == "all... | tools/check_uptimes.py | import subprocess, sys, itertools, getpass
username = getpass.getuser()
usage_error = False
allservers = ["maris%03i" % i for i in range(2, 68)]
if len(sys.argv) == 2:
if sys.argv[1].lower() == "default":
servers = ["maris%03i" % i for i in range(25,33) + range(61,68)]
elif sys.argv[1].lower() == "all... | 0.065239 | 0.094929 |
import typing as ty
import builtins
import itertools
from collections.abc import Iterator, AsyncIterator
import pytest
from hypothesis import given, assume, strategies as st
import none
#: Maximum range stop value not to have infinite loop tests.
MAX_RANGE = (2 ** 13) - 1
@pytest.fixture
def arange() -> ty.Type... | tests/collection/test_a.py | import typing as ty
import builtins
import itertools
from collections.abc import Iterator, AsyncIterator
import pytest
from hypothesis import given, assume, strategies as st
import none
#: Maximum range stop value not to have infinite loop tests.
MAX_RANGE = (2 ** 13) - 1
@pytest.fixture
def arange() -> ty.Type... | 0.807347 | 0.419232 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['JobScheduleArgs', 'JobSchedule']
@pulumi.input_type
class JobScheduleArgs:
def __init__(__self__, *,
automation_account_name: pulumi.Inpu... | sdk/python/pulumi_azure/automation/job_schedule.py |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from .. import _utilities
__all__ = ['JobScheduleArgs', 'JobSchedule']
@pulumi.input_type
class JobScheduleArgs:
def __init__(__self__, *,
automation_account_name: pulumi.Inpu... | 0.847369 | 0.090253 |
class Solution:
def neighbours(self, board, pixel):
neighbours = set()
row = pixel[0]
col = pixel[1]
ROW = len(board)
COL = len(board[0])
if row - 1 > -1 and board[row-1][col] == 'O':
neighbours.add((row-1, col))
if row + 1 < ROW and board[row+1][c... | python/surround_region.py | class Solution:
def neighbours(self, board, pixel):
neighbours = set()
row = pixel[0]
col = pixel[1]
ROW = len(board)
COL = len(board[0])
if row - 1 > -1 and board[row-1][col] == 'O':
neighbours.add((row-1, col))
if row + 1 < ROW and board[row+1][c... | 0.672117 | 0.576363 |
import os
import time
import json
import torch
import dill
import random
import pathlib
import evaluation
import numpy as np
import visualization as vis
from argument_parser import args
from model.online.online_trajectron import OnlineTrajectron
from model.model_registrar import ModelRegistrar
from environment import E... | trajectron/test_online.py | import os
import time
import json
import torch
import dill
import random
import pathlib
import evaluation
import numpy as np
import visualization as vis
from argument_parser import args
from model.online.online_trajectron import OnlineTrajectron
from model.model_registrar import ModelRegistrar
from environment import E... | 0.437343 | 0.295681 |
import os, sys
import numpy as np
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
#-------------------------------------------------------
# params
#-------------------------------------------------------
name = sys.argv[1]
i_wave = int(sys.argv[2])
nbatch = int(sys.arg... | bin/decoder.py | import os, sys
import numpy as np
import torch
from torch import nn
from torch import optim
from torch.nn import functional as F
#-------------------------------------------------------
# params
#-------------------------------------------------------
name = sys.argv[1]
i_wave = int(sys.argv[2])
nbatch = int(sys.arg... | 0.596786 | 0.210726 |
# This piece of code detects whether motion has been detected on the respective pir motion
# sensor and determines whether someone is entering a room or leaving a room, relays that
# information as occupancy level
import esp
import machine
import connectWifi
import time
import config
import utime
import json
from ... | esp/motion-sensor/main.py |
# This piece of code detects whether motion has been detected on the respective pir motion
# sensor and determines whether someone is entering a room or leaving a room, relays that
# information as occupancy level
import esp
import machine
import connectWifi
import time
import config
import utime
import json
from ... | 0.461502 | 0.379608 |
import sys
import argparse
from typing import List, Optional
from .lib import FileHelper
from .lib import TerminalStyle
from .converter.ConverterInterface import ConverterInterface
from .model.IntermediateLocalization import IntermediateLocalization
from .model.LocalizationFile import LocalizationFile
#------------... | Logen/main_subcommand_convert.py | import sys
import argparse
from typing import List, Optional
from .lib import FileHelper
from .lib import TerminalStyle
from .converter.ConverterInterface import ConverterInterface
from .model.IntermediateLocalization import IntermediateLocalization
from .model.LocalizationFile import LocalizationFile
#------------... | 0.47244 | 0.149718 |
import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score
from torch.utils.data import TensorDataset
from aijack.attack import M... | example/model_inversion/mi_face_differential_privacy.py | import cv2
import numpy as np
import torch
import torch.nn as nn
import torch.nn.functional as F
import torch.optim as optim
import torchvision.transforms as transforms
from matplotlib import pyplot as plt
from sklearn.metrics import accuracy_score
from torch.utils.data import TensorDataset
from aijack.attack import M... | 0.925196 | 0.490785 |
import json
try:
import pika
except Exception as e:
print("Some modules are missing!", e)
class RabbitMqConfig(object):
"""
This class configures the rabbit mq
"""
def __init__(self, host='localhost', queue='Hello'):
"""
This method is used to initialise the server... | Source/GUI_2/receiver.py | import json
try:
import pika
except Exception as e:
print("Some modules are missing!", e)
class RabbitMqConfig(object):
"""
This class configures the rabbit mq
"""
def __init__(self, host='localhost', queue='Hello'):
"""
This method is used to initialise the server... | 0.481941 | 0.274461 |
from orochi.website.models import UserPlugin
from django.contrib.auth import get_user_model
from django.contrib.auth.mixins import LoginRequiredMixin
from django.urls import reverse
from django.utils.translation import gettext_lazy as _
from django.views.generic import RedirectView, DetailView
from django.shortcuts imp... | orochi/users/views.py | from orochi.website.models import UserPlugin
from django.contrib.auth import get_user_model
from django.contrib.auth.mixins import LoginRequiredMixin
from django.urls import reverse
from django.utils.translation import gettext_lazy as _
from django.views.generic import RedirectView, DetailView
from django.shortcuts imp... | 0.416797 | 0.075007 |
import torch
import torch.nn as nn
import numpy as np
import os
import matplotlib
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# The squash function specified in Dynamic Routing Between Capsules
# x: i... | tools.py | import torch
import torch.nn as nn
import numpy as np
import os
import matplotlib
if os.environ.get('DISPLAY','') == '':
print('no display found. Using non-interactive Agg backend')
matplotlib.use('Agg')
import matplotlib.pyplot as plt
# The squash function specified in Dynamic Routing Between Capsules
# x: i... | 0.628635 | 0.449936 |
import unittest
def rotate_matrix(matrix):
"""
Swap layer by layer, from outside to inside. Observe the way i and j change to have a appropriate formula
:param matrix:
:return:
"""
if not matrix:
return
n = len(matrix)
for i in range(n // 2):
for j in range(i, n - i -... | ArraysAndStrings/1_7_RotateMatrix.py | import unittest
def rotate_matrix(matrix):
"""
Swap layer by layer, from outside to inside. Observe the way i and j change to have a appropriate formula
:param matrix:
:return:
"""
if not matrix:
return
n = len(matrix)
for i in range(n // 2):
for j in range(i, n - i -... | 0.735357 | 0.796925 |
from __future__ import annotations
import subprocess as sp
from ..environment import (
Environment,
Dependency,
HeaderDependency,
LibraryDependency,
ProgramDependency,
Installer,
GNUInstaller,
GurobiInstaller,
)
from ...errors import InstallError, UninstallError
mipverify_runner = """... | dnnv/_manage/linux/verifiers/mipverify.py | from __future__ import annotations
import subprocess as sp
from ..environment import (
Environment,
Dependency,
HeaderDependency,
LibraryDependency,
ProgramDependency,
Installer,
GNUInstaller,
GurobiInstaller,
)
from ...errors import InstallError, UninstallError
mipverify_runner = """... | 0.446253 | 0.116136 |
from flask_restful_swagger import swagger
from flask_restful import fields
@swagger.model
class HumanSerializer:
resource_fields = {
'reanimators_count': fields.Integer,
'need_staff': fields.Integer,
}
@swagger.model
class BedSerializer:
resource_fields = {
'covid_available': fie... | app/covidbed/serializer/resource.py | from flask_restful_swagger import swagger
from flask_restful import fields
@swagger.model
class HumanSerializer:
resource_fields = {
'reanimators_count': fields.Integer,
'need_staff': fields.Integer,
}
@swagger.model
class BedSerializer:
resource_fields = {
'covid_available': fie... | 0.733261 | 0.135546 |
import logging
from typing import List, Optional, Union
from xml.etree.ElementTree import Element
from jmeter_api.basics.config.elements import BasicConfig
from jmeter_api.basics.utils import Renderable, FileEncoding, tree_to_str
class Header(Renderable):
TEMPLATE = 'header.xml'
root_element_name = 'el... | jmeter_api/configs/http_header_manager/elements.py | import logging
from typing import List, Optional, Union
from xml.etree.ElementTree import Element
from jmeter_api.basics.config.elements import BasicConfig
from jmeter_api.basics.utils import Renderable, FileEncoding, tree_to_str
class Header(Renderable):
TEMPLATE = 'header.xml'
root_element_name = 'el... | 0.770508 | 0.190799 |
import asyncio
import logging
import threading
from datetime import timedelta
from typing import Optional
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import CONF_HOST, CONF_NAME, CONF_PORT, CONF_SCAN_INTERVA... | solax_modbus/__init__.py | import asyncio
import logging
import threading
from datetime import timedelta
from typing import Optional
import homeassistant.helpers.config_validation as cv
import voluptuous as vol
from homeassistant.config_entries import ConfigEntry
from homeassistant.const import CONF_HOST, CONF_NAME, CONF_PORT, CONF_SCAN_INTERVA... | 0.787114 | 0.119562 |
import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import numpy as np
class Classification_Dataloader(Dataset):
def __init__(self, hands_lines):
super(Classification_Dataloader, self).__init__()
self.hand... | classification.py | import torch
import torch.nn as nn
from torch import optim
from torch.utils.data import DataLoader
from torch.utils.data.dataset import Dataset
import numpy as np
class Classification_Dataloader(Dataset):
def __init__(self, hands_lines):
super(Classification_Dataloader, self).__init__()
self.hand... | 0.844922 | 0.604516 |
import random
import pandas as pd
import io
import json
from dateutil import parser
from collections import OrderedDict
import time
import csv
import difflib
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import os
import sys, traceback
from matplotlib.legend_handler import HandlerLine2D
import gr... | experiments/models/geo.py | import random
import pandas as pd
import io
import json
from dateutil import parser
from collections import OrderedDict
import time
import csv
import difflib
import matplotlib
import numpy as np
import matplotlib.pyplot as plt
import os
import sys, traceback
from matplotlib.legend_handler import HandlerLine2D
import gr... | 0.455199 | 0.346928 |
import argparse
import tensorflow as tf
import tensorrt as trt
import uff
import graphsurgeon as gs
import converter_util
def setup_args(parser):
parser.add_argument("--input", "-i", help="Path to input file", required=True, type=str)
parser.add_argument("--input_dims", "-id", help="Dimensions of input tenso... | tensorrt_converter.py | import argparse
import tensorflow as tf
import tensorrt as trt
import uff
import graphsurgeon as gs
import converter_util
def setup_args(parser):
parser.add_argument("--input", "-i", help="Path to input file", required=True, type=str)
parser.add_argument("--input_dims", "-id", help="Dimensions of input tenso... | 0.31363 | 0.314524 |
from pygroupsig.common_build import ffibuilder
ffibuilder.cdef("""
typedef struct {
uint8_t scheme;
void *sig;
} groupsig_signature_t;
""")
ffibuilder.cdef("""
typedef groupsig_signature_t* (*groupsig_signature_init_f)(void);
""")
ffibuilder.cdef("""
typedef int (*groupsig_signature_free_f)(groupsig_signature_t *si... | src/wrappers/python/pygroupsig/signature_build.py |
from pygroupsig.common_build import ffibuilder
ffibuilder.cdef("""
typedef struct {
uint8_t scheme;
void *sig;
} groupsig_signature_t;
""")
ffibuilder.cdef("""
typedef groupsig_signature_t* (*groupsig_signature_init_f)(void);
""")
ffibuilder.cdef("""
typedef int (*groupsig_signature_free_f)(groupsig_signature_t *si... | 0.541894 | 0.051702 |
import pandas as pd
import logging
from dowhy.do_why import CausalModel
import dowhy.do_samplers as do_samplers
class CausalDataFrame(pd.DataFrame):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._causal_model = None
self._sampler = None
self._identifie... | dowhy/api/causal_data_frame.py | import pandas as pd
import logging
from dowhy.do_why import CausalModel
import dowhy.do_samplers as do_samplers
class CausalDataFrame(pd.DataFrame):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
self._causal_model = None
self._sampler = None
self._identifie... | 0.557123 | 0.092319 |
from rest_framework.views import APIView
from rest_framework.exceptions import APIException, NotFound
from rest_framework.response import Response
from rest_framework.authentication import TokenAuthentication
from rest_framework.permissions import IsAuthenticated
from returntoclinicstation.models import *
from datetim... | returntoclinicstation/views.py |
from rest_framework.views import APIView
from rest_framework.exceptions import APIException, NotFound
from rest_framework.response import Response
from rest_framework.authentication import TokenAuthentication
from rest_framework.permissions import IsAuthenticated
from returntoclinicstation.models import *
from datetim... | 0.344003 | 0.061621 |
import pytest
import numpy as np
from numpy.testing import assert_allclose
import json
import os
import inspect
# Importing auto_HU_NJ module
from auto_HU_NJ import *
# Current directory
cur_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# Test input directory
base_input_path = 'resourc... | pelicun/resources/auto_population/tests/test_auto_HU_NJ.py | import pytest
import numpy as np
from numpy.testing import assert_allclose
import json
import os
import inspect
# Importing auto_HU_NJ module
from auto_HU_NJ import *
# Current directory
cur_dir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
# Test input directory
base_input_path = 'resourc... | 0.295636 | 0.320808 |
from django.db import migrations, transaction
class Migration(migrations.Migration):
dependencies = [
("App", "0002_user_test_data"),
]
def generate_data(apps, schema_editor):
from App.models import Website, Profile
from django.shortcuts import get_object_or_404
website_t... | App/migrations/0003_website_test_data.py | from django.db import migrations, transaction
class Migration(migrations.Migration):
dependencies = [
("App", "0002_user_test_data"),
]
def generate_data(apps, schema_editor):
from App.models import Website, Profile
from django.shortcuts import get_object_or_404
website_t... | 0.33764 | 0.210502 |
import pytest
from pyidxp.aws.s3 import S3
from boto.s3.connection import OrdinaryCallingFormat
class FakeS3Connection:
__ref__ = None
def __init__(self, region, aws_access_key_id=None,
aws_secret_access_key=None, calling_format=None):
self.__class__.__ref__ = self
self.conn_... | tests/aws/test_s3.py | import pytest
from pyidxp.aws.s3 import S3
from boto.s3.connection import OrdinaryCallingFormat
class FakeS3Connection:
__ref__ = None
def __init__(self, region, aws_access_key_id=None,
aws_secret_access_key=None, calling_format=None):
self.__class__.__ref__ = self
self.conn_... | 0.578686 | 0.128908 |
import json
import traceback
from django.http import JsonResponse
from ..models import ApigeeMgmtLog
from ..env import Env
from ..utils import REQUEST_KEYS_NO_ARTIFACTS
from ..validate import validate_payload, ValidationException
from .sharedflows import migrate_shared_flows
from .proxies import migrate_proxies
from ... | api/migrate/migrate.py | import json
import traceback
from django.http import JsonResponse
from ..models import ApigeeMgmtLog
from ..env import Env
from ..utils import REQUEST_KEYS_NO_ARTIFACTS
from ..validate import validate_payload, ValidationException
from .sharedflows import migrate_shared_flows
from .proxies import migrate_proxies
from ... | 0.273186 | 0.112893 |
import time
import github
from loguru import logger
from typing import List
class GithubWrapper:
def __init__(self, token, throttle_secs):
self.gh = github.Github(token)
self.cache = {}
self.throttle_secs = throttle_secs
self.get_repo_cache_miss_count = 0
self.get_repo_cach... | src/library/ghw.py | import time
import github
from loguru import logger
from typing import List
class GithubWrapper:
def __init__(self, token, throttle_secs):
self.gh = github.Github(token)
self.cache = {}
self.throttle_secs = throttle_secs
self.get_repo_cache_miss_count = 0
self.get_repo_cach... | 0.382949 | 0.077588 |
from PyQt5 import QtWidgets
from PyQt5.QtCore import QRegExp, pyqtSlot
from PyQt5.QtGui import QRegExpValidator, QValidator
import os
import sys
from lib.settings import SETTINGS, get_language_versions, get_model_languages, update_settings
from lib.utils.crypt import Crypt
VALID = 2 # value of state enum representin... | src/main/python/settings_view.py | from PyQt5 import QtWidgets
from PyQt5.QtCore import QRegExp, pyqtSlot
from PyQt5.QtGui import QRegExpValidator, QValidator
import os
import sys
from lib.settings import SETTINGS, get_language_versions, get_model_languages, update_settings
from lib.utils.crypt import Crypt
VALID = 2 # value of state enum representin... | 0.454714 | 0.147156 |
__author__ = '<NAME>'
from typing import List, Set, Type, Iterable, Tuple
from dataclasses import dataclass, field
import logging
from spacy.tokens.doc import Doc
from spacy.tokens.span import Span
from zensols.config import Dictable
from . import (
ParseError, LanguageResource, TokenFeatures,
FeatureToken, Fe... | src/python/zensols/nlp/docparser.py | __author__ = '<NAME>'
from typing import List, Set, Type, Iterable, Tuple
from dataclasses import dataclass, field
import logging
from spacy.tokens.doc import Doc
from spacy.tokens.span import Span
from zensols.config import Dictable
from . import (
ParseError, LanguageResource, TokenFeatures,
FeatureToken, Fe... | 0.883883 | 0.390708 |
import argparse
import os
import tempfile
import time
import typing
import webbrowser
from difflib import SequenceMatcher
from pathlib import Path
from sys import platform
from warnings import warn
import easyocr
import numpy as np
from mss import mss
from mutagen.wave import WAVE
from PIL import Image
from sclog impo... | transcribe_chrome/src/audio2text.py | import argparse
import os
import tempfile
import time
import typing
import webbrowser
from difflib import SequenceMatcher
from pathlib import Path
from sys import platform
from warnings import warn
import easyocr
import numpy as np
from mss import mss
from mutagen.wave import WAVE
from PIL import Image
from sclog impo... | 0.228759 | 0.148355 |
SNAKE_TO_CAMEL_CASE_TABLE = {
"access_ip_v4": "accessIpV4",
"access_ip_v6": "accessIpV6",
"access_key": "accessKey",
"access_level": "accessLevel",
"access_rules": "accessRules",
"access_to": "accessTo",
"access_type": "accessType",
"address_scope_id": "addressScopeId",
"admin_pass"... | sdk/python/pulumi_openstack/_tables.py |
SNAKE_TO_CAMEL_CASE_TABLE = {
"access_ip_v4": "accessIpV4",
"access_ip_v6": "accessIpV6",
"access_key": "accessKey",
"access_level": "accessLevel",
"access_rules": "accessRules",
"access_to": "accessTo",
"access_type": "accessType",
"address_scope_id": "addressScopeId",
"admin_pass"... | 0.395835 | 0.273077 |
from pyspark.ml.wrapper import JavaTransformer
from pyspark.ml.util import _jvm
from pyspark.ml.param import *
class ScoreModel(JavaTransformer):
"""
Model restored from PMML.
"""
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name.",
typeConverte... | pypmml_spark/score_model.py |
from pyspark.ml.wrapper import JavaTransformer
from pyspark.ml.util import _jvm
from pyspark.ml.param import *
class ScoreModel(JavaTransformer):
"""
Model restored from PMML.
"""
predictionCol = Param(Params._dummy(), "predictionCol", "prediction column name.",
typeConverte... | 0.921087 | 0.381594 |
import pprint
import re # noqa: F401
import six
class Model(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is... | leiaapi/generated/models/model.py | import pprint
import re # noqa: F401
import six
class Model(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
"""
"""
Attributes:
swagger_types (dict): The key is attribute name
and the value is... | 0.597256 | 0.104569 |
from __future__ import print_function
from collections import defaultdict
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.ensemble import BaggingClassifier
from sklearn.metrics import accuracy_score
from .mdr import MDR
from ._version import __version__
class MDREnsemble(Ba... | mdr/mdr_ensemble.py | from __future__ import print_function
from collections import defaultdict
import numpy as np
from sklearn.base import BaseEstimator, ClassifierMixin
from sklearn.ensemble import BaggingClassifier
from sklearn.metrics import accuracy_score
from .mdr import MDR
from ._version import __version__
class MDREnsemble(Ba... | 0.924022 | 0.352592 |
import pprint
import re # noqa: F401
import six
from pycherwell.configuration import Configuration
class AttachmentsRequest(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
op... | pycherwell/models/attachments_request.py | import pprint
import re # noqa: F401
import six
from pycherwell.configuration import Configuration
class AttachmentsRequest(object):
"""NOTE: This class is auto generated by OpenAPI Generator.
Ref: https://openapi-generator.tech
Do not edit the class manually.
"""
"""
Attributes:
op... | 0.616359 | 0.067362 |
import pandas as pd
import numpy as np
from logging_utils import log_exception, log_info, log_warn
def loadFile(filename):
"""
** Function loadFile** loads the content of a file into a python dataframe
Allowed file formats are TXT (.txt), CSV (.csv), and Excel (.xls or xlsx)
**parameters**:
... | Utils.py | import pandas as pd
import numpy as np
from logging_utils import log_exception, log_info, log_warn
def loadFile(filename):
"""
** Function loadFile** loads the content of a file into a python dataframe
Allowed file formats are TXT (.txt), CSV (.csv), and Excel (.xls or xlsx)
**parameters**:
... | 0.245175 | 0.245661 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = [
'NetworkAssignIpv4Args',
'NetworkAssignIpv6Args',
'NetworkAssignmentPoolArgs',
'NetworkRouteArgs',
'GetNetworkAssignIpv4Args',
'GetNetwo... | sdk/python/pulumi_zerotier/_inputs.py |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union, overload
from . import _utilities
__all__ = [
'NetworkAssignIpv4Args',
'NetworkAssignIpv6Args',
'NetworkAssignmentPoolArgs',
'NetworkRouteArgs',
'GetNetworkAssignIpv4Args',
'GetNetwo... | 0.858659 | 0.102754 |
import os
os.chdir(r'working directory')
import matplotlib.image as img
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
# =============================================================================
# ===========================================... | Mini_DViz_Portfolio.py |
import os
os.chdir(r'working directory')
import matplotlib.image as img
import matplotlib.pyplot as plt
from matplotlib.offsetbox import OffsetImage, AnnotationBbox
import numpy as np
# =============================================================================
# ===========================================... | 0.592549 | 0.427576 |
import time
import datetime
import botlib
from javascript import require, On, Once, AsyncTask, once, off
pathfinder = require('mineflayer-pathfinder')
class ChatBot:
stopActivity = True
activity_start = 0
activity_name = "None"
activity_major = False
activity_last_duration = 0
def __init__(... | chat.py |
import time
import datetime
import botlib
from javascript import require, On, Once, AsyncTask, once, off
pathfinder = require('mineflayer-pathfinder')
class ChatBot:
stopActivity = True
activity_start = 0
activity_name = "None"
activity_major = False
activity_last_duration = 0
def __init__(... | 0.311113 | 0.167968 |
import os.path
from os import path
import sys
from pprint import pprint
from direct.showbase.ShowBase import ShowBase
from direct.task import Task
from direct.actor.Actor import Actor
from direct.interval.IntervalGlobal import Sequence
from panda3d.core import Point3
from direct.gui.OnscreenText import OnscreenText
f... | p3danimall.py | import os.path
from os import path
import sys
from pprint import pprint
from direct.showbase.ShowBase import ShowBase
from direct.task import Task
from direct.actor.Actor import Actor
from direct.interval.IntervalGlobal import Sequence
from panda3d.core import Point3
from direct.gui.OnscreenText import OnscreenText
f... | 0.083148 | 0.056288 |
from math import sqrt
#КРУГ
#События
class Circle:
def __init__(self, par):
self.par = par
self.risCircle()
def risCircle(self):
self.par.kill()
self.par.standart_unbind()
self.par.old_func = 'self.risCircle()'
self.par.c.bind('<Button-1>', self.circle)
... | src/circle.py | from math import sqrt
#КРУГ
#События
class Circle:
def __init__(self, par):
self.par = par
self.risCircle()
def risCircle(self):
self.par.kill()
self.par.standart_unbind()
self.par.old_func = 'self.risCircle()'
self.par.c.bind('<Button-1>', self.circle)
... | 0.165728 | 0.126974 |
import xmlrpc.client
import ssl
import socket # Required for network/socket connections
import os # Required for Forking/child processes
import time # Required for sleep call
import threading # Required for communication sub-threads
import pymysql
import server_monitor as myServer
import certs.gencer... | monitor.py | import xmlrpc.client
import ssl
import socket # Required for network/socket connections
import os # Required for Forking/child processes
import time # Required for sleep call
import threading # Required for communication sub-threads
import pymysql
import server_monitor as myServer
import certs.gencer... | 0.426799 | 0.066995 |
from __future__ import unicode_literals
from __future__ import print_function
import logging
import numpy as np
from . import SampleBasedDecay
logger = logging.getLogger('decay.sigmoid')
class SigmoidDecay(SampleBasedDecay):
"""
Class that decays the value following the sigmoid curve.
Sigmoid is:
... | decay/decays/sample/sigmoid.py | from __future__ import unicode_literals
from __future__ import print_function
import logging
import numpy as np
from . import SampleBasedDecay
logger = logging.getLogger('decay.sigmoid')
class SigmoidDecay(SampleBasedDecay):
"""
Class that decays the value following the sigmoid curve.
Sigmoid is:
... | 0.815233 | 0.229125 |
import os
from pylearn2ext.chbmit import CHBMIT
from pylearn2ext.epilepsiae import EpilepsiaeTest
def compute_n_samples_chbmit():
patients = [1, 3, 5, 8, 10, 20]
model_path = '../models'
data_path = '/Users/akara/Workspace/data/chbmit'
with open(os.path.join(model_path, 'sdae_chbmit_train_test_samples... | seizure detection code/Stacked Autoencoders for Seizure Detection/tests/compute_num_samples.py | import os
from pylearn2ext.chbmit import CHBMIT
from pylearn2ext.epilepsiae import EpilepsiaeTest
def compute_n_samples_chbmit():
patients = [1, 3, 5, 8, 10, 20]
model_path = '../models'
data_path = '/Users/akara/Workspace/data/chbmit'
with open(os.path.join(model_path, 'sdae_chbmit_train_test_samples... | 0.250363 | 0.194884 |
import contextlib
import os
import shlex
import sys
import subprocess
import tempfile
from pathlib import Path
# Branhes to backport to, in order from master, without master
FBRANCHES = ['f34', 'f33', 'f32', 'f31']
# Colors
BLUE = '\033[94m'
GREEN = '\033[92m'
END = '\033[0m'
# Component swaps
COMPONENTS = {
'p... | branchsync.py |
import contextlib
import os
import shlex
import sys
import subprocess
import tempfile
from pathlib import Path
# Branhes to backport to, in order from master, without master
FBRANCHES = ['f34', 'f33', 'f32', 'f31']
# Colors
BLUE = '\033[94m'
GREEN = '\033[92m'
END = '\033[0m'
# Component swaps
COMPONENTS = {
'p... | 0.173778 | 0.096791 |
import json
import logging
import boto3
from data_access.data_config import LOG_LEVEL
from botocore.exceptions import ClientError
from decimal import Decimal
logger = logging.getLogger('DDB_Utils')
logger.setLevel(LOG_LEVEL)
dynamodb = boto3.resource('dynamodb')
def convert_num_to_dec(num):
"""
Convert a n... | lambda/src/data_access/ddb_util.py | import json
import logging
import boto3
from data_access.data_config import LOG_LEVEL
from botocore.exceptions import ClientError
from decimal import Decimal
logger = logging.getLogger('DDB_Utils')
logger.setLevel(LOG_LEVEL)
dynamodb = boto3.resource('dynamodb')
def convert_num_to_dec(num):
"""
Convert a n... | 0.480479 | 0.109873 |
from typing import List, Dict, Union
import numpy as np
from overrides import final
from ikpy.chain import Chain
from ikpy.link import OriginLink, URDFLink, Link
from tdw.tdw_utils import TDWUtils
from tdw.quaternion_utils import QuaternionUtils
from tdw.add_ons.robot import Robot
from tdw.librarian import RobotLibrari... | Python/tdw/add_ons/robot_arm.py | from typing import List, Dict, Union
import numpy as np
from overrides import final
from ikpy.chain import Chain
from ikpy.link import OriginLink, URDFLink, Link
from tdw.tdw_utils import TDWUtils
from tdw.quaternion_utils import QuaternionUtils
from tdw.add_ons.robot import Robot
from tdw.librarian import RobotLibrari... | 0.971497 | 0.660323 |
from freetype import *
def arrow( x,y, dx, dy, **kwargs):
kwargs['shape'] = 'full'
kwargs['head_width'] = 30
kwargs['head_length'] = 40
kwargs['length_includes_head'] =True
kwargs['facecolor'] = 'k'
kwargs['edgecolor'] ='k'
kwargs['linewidth'] =.5
plt.arrow(x,y,dx,dy,**kwargs)
def doub... | examples/glyph-metrics.py | from freetype import *
def arrow( x,y, dx, dy, **kwargs):
kwargs['shape'] = 'full'
kwargs['head_width'] = 30
kwargs['head_length'] = 40
kwargs['length_includes_head'] =True
kwargs['facecolor'] = 'k'
kwargs['edgecolor'] ='k'
kwargs['linewidth'] =.5
plt.arrow(x,y,dx,dy,**kwargs)
def doub... | 0.403214 | 0.294526 |
from html.parser import HTMLParser
import json
class TableMiningParser(HTMLParser):
def __init__(self):
super().__init__()
self.n_tbody = 0
self.content_list = []
self.content_row = []
self.in_header_row = False
self.in_tr = False
self.in_td = False
... | assets/extract_to_json.py | from html.parser import HTMLParser
import json
class TableMiningParser(HTMLParser):
def __init__(self):
super().__init__()
self.n_tbody = 0
self.content_list = []
self.content_row = []
self.in_header_row = False
self.in_tr = False
self.in_td = False
... | 0.45302 | 0.160135 |
import sys, os, os.path
import subprocess
import ctypes
def name2lib(name):
"""Convert a name 'foo' into the OS dependent library name::
libfoo.so
libfoo.dylib
foo.dll
"""
_prefix = "" if os.name == 'nt' else 'lib'
_dll = "dll" if os.name == '.nt' else '.so'
if sys.platform... | src/clients/python/trtis_cidmgr/util.py |
import sys, os, os.path
import subprocess
import ctypes
def name2lib(name):
"""Convert a name 'foo' into the OS dependent library name::
libfoo.so
libfoo.dylib
foo.dll
"""
_prefix = "" if os.name == 'nt' else 'lib'
_dll = "dll" if os.name == '.nt' else '.so'
if sys.platform... | 0.349422 | 0.082846 |
from cleandata import tag_to_pos
from cleandata import pos_to_tag
from cleandata import tagid
from cleandata import tagname
import pandas as pd
from collections import defaultdict
import math
import numpy as np
import itertools
import csv
import sys
import gc
from heapq import nlargest
comm = defaultdict(lambda: defau... | Ontology.py | from cleandata import tag_to_pos
from cleandata import pos_to_tag
from cleandata import tagid
from cleandata import tagname
import pandas as pd
from collections import defaultdict
import math
import numpy as np
import itertools
import csv
import sys
import gc
from heapq import nlargest
comm = defaultdict(lambda: defau... | 0.046965 | 0.522811 |
from flask import Flask
from flask_sockets import Sockets
import json
app = Flask(__name__)
sockets = Sockets(app)
devices = {} # A dictionary of id to devices
SERVER_CONFIG = {"ice_servers": [{"urls":["stun:stun.l.google.com:19302"]}],
"message_type": "config"}
def send_error(msg):
print('ERRO... | sig_server.py | from flask import Flask
from flask_sockets import Sockets
import json
app = Flask(__name__)
sockets = Sockets(app)
devices = {} # A dictionary of id to devices
SERVER_CONFIG = {"ice_servers": [{"urls":["stun:stun.l.google.com:19302"]}],
"message_type": "config"}
def send_error(msg):
print('ERRO... | 0.417984 | 0.053849 |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Index', '0002_indexpageviewkeybenfitsmodel_sort_id'),
]
operations = [
migrations.CreateModel(
name='IndexPageQAModel',
fields=[
('id', models.AutoF... | ARMODServers/Apps/Index/migrations/0003_auto_20210420_2141.py |
from django.db import migrations, models
class Migration(migrations.Migration):
dependencies = [
('Index', '0002_indexpageviewkeybenfitsmodel_sort_id'),
]
operations = [
migrations.CreateModel(
name='IndexPageQAModel',
fields=[
('id', models.AutoF... | 0.551574 | 0.149469 |
import snake
import pygame as p
class OpSnake(snake.Snake):
"""
[0-3]
00 distWEsq,
01 distWCim,
02 distWDir,
03 distWBai,
[4-7]
04 distWCimEsq,
05 distWCimDir,
06 distWBaiEsq,
07 distWBaiDir
[8-11]
08 distEsq,
09 distCim,
10 distDir,
11 distBai
[12-15]... | opsnake.py | import snake
import pygame as p
class OpSnake(snake.Snake):
"""
[0-3]
00 distWEsq,
01 distWCim,
02 distWDir,
03 distWBai,
[4-7]
04 distWCimEsq,
05 distWCimDir,
06 distWBaiEsq,
07 distWBaiDir
[8-11]
08 distEsq,
09 distCim,
10 distDir,
11 distBai
[12-15]... | 0.217587 | 0.331498 |
from __future__ import annotations
import typing
import zserio
import tutorial.experience
import tutorial.role
class Employee:
def __init__(
self,
age_: int = int(),
name_: str = str(),
salary_: int = int(),
bonus_: typing.Optional[int] = None,
... | src/tutorial/employee.py | from __future__ import annotations
import typing
import zserio
import tutorial.experience
import tutorial.role
class Employee:
def __init__(
self,
age_: int = int(),
name_: str = str(),
salary_: int = int(),
bonus_: typing.Optional[int] = None,
... | 0.782579 | 0.26815 |
from rest_framework import serializers
from groupon.services import create_groupon, update_groupon
from wsc_django.utils.constant import DateFormat
from wsc_django.utils.core import FuncField
class AdminGrouponCreateSerializer(serializers.Serializer):
"""后台拼团活动创建序列化器"""
price = FuncField(lambda value: round... | wsc_django/wsc_django/apps/groupon/serializers.py | from rest_framework import serializers
from groupon.services import create_groupon, update_groupon
from wsc_django.utils.constant import DateFormat
from wsc_django.utils.core import FuncField
class AdminGrouponCreateSerializer(serializers.Serializer):
"""后台拼团活动创建序列化器"""
price = FuncField(lambda value: round... | 0.551091 | 0.233073 |
import sys
import socket
import ipaddress
import requests
from urllib.parse import urlparse
from tools.EMAIL.emailTools import ReadSenderEmail
from time import sleep
from colorama import Fore
""" Check if site is under CloudFlare protection """
def __isCloudFlare(link):
parsed_uri = urlparse(link)
domain = "{... | tools/ipTools.py | import sys
import socket
import ipaddress
import requests
from urllib.parse import urlparse
from tools.EMAIL.emailTools import ReadSenderEmail
from time import sleep
from colorama import Fore
""" Check if site is under CloudFlare protection """
def __isCloudFlare(link):
parsed_uri = urlparse(link)
domain = "{... | 0.111241 | 0.164449 |
import queue
import time
import numpy as np
import voluptuous as vol
from ledfx.color import COLORS, GRADIENTS
from ledfx.effects.audio import AudioReactiveEffect
from ledfx.effects.gradient import GradientEffect
class Strobe(AudioReactiveEffect, GradientEffect):
NAME = "Real Strobe"
CONFIG_SCHEMA = vol.Sc... | ledfx/effects/real_strobe(Reactive).py | import queue
import time
import numpy as np
import voluptuous as vol
from ledfx.color import COLORS, GRADIENTS
from ledfx.effects.audio import AudioReactiveEffect
from ledfx.effects.gradient import GradientEffect
class Strobe(AudioReactiveEffect, GradientEffect):
NAME = "Real Strobe"
CONFIG_SCHEMA = vol.Sc... | 0.512449 | 0.24178 |
import collections
class CyclicGraphError(Exception):
pass
class Digraph(object):
"""An acyclic, directed graph.
>>> g = Digraph()
>>> g.add_edge('a', 'b')
>>> g.add_edge('a', 'c')
>>> g.add_edge('b', 'c')
You can use a digraph to compute topological orderings (eg. ... | sparkplug/digraph.py | import collections
class CyclicGraphError(Exception):
pass
class Digraph(object):
"""An acyclic, directed graph.
>>> g = Digraph()
>>> g.add_edge('a', 'b')
>>> g.add_edge('a', 'c')
>>> g.add_edge('b', 'c')
You can use a digraph to compute topological orderings (eg. ... | 0.788217 | 0.27443 |
import sys
import os
import logging
from glob import iglob
from collections import Counter, OrderedDict
def readable_file(fn):
"""Check if the file is readable"""
fn = os.path.abspath(fn)
if not os.path.isfile (fn) or not os.access (fn, os.R_OK):
raise ont2cramError("File '{}' does not exist or is... | ont2cram/common.py |
import sys
import os
import logging
from glob import iglob
from collections import Counter, OrderedDict
def readable_file(fn):
"""Check if the file is readable"""
fn = os.path.abspath(fn)
if not os.path.isfile (fn) or not os.access (fn, os.R_OK):
raise ont2cramError("File '{}' does not exist or is... | 0.295636 | 0.069605 |
import agentos
import click
from datetime import datetime
import gym
import mlflow.projects
import importlib.util
from pathlib import Path
CONDA_ENV_FILE = Path("./conda_env.yaml")
CONDA_ENV_CONTENT = """{file_header}
name: {name}
dependencies:
- pip
- pip:
- gym
- agentos
# Or, if you wan... | agentos/cli.py | import agentos
import click
from datetime import datetime
import gym
import mlflow.projects
import importlib.util
from pathlib import Path
CONDA_ENV_FILE = Path("./conda_env.yaml")
CONDA_ENV_CONTENT = """{file_header}
name: {name}
dependencies:
- pip
- pip:
- gym
- agentos
# Or, if you wan... | 0.428951 | 0.227191 |
from django.db import models
from django.utils import timezone
from django.utils.text import slugify
from django.utils.safestring import mark_safe
from django.core.urlresolvers import reverse
from .markup import markup
class CategoryQuerySet(models.QuerySet):
def search(self, term):
return self.filter(nam... | blog/models.py | from django.db import models
from django.utils import timezone
from django.utils.text import slugify
from django.utils.safestring import mark_safe
from django.core.urlresolvers import reverse
from .markup import markup
class CategoryQuerySet(models.QuerySet):
def search(self, term):
return self.filter(nam... | 0.529993 | 0.123445 |
from __future__ import annotations
import difflib
import re
from collections import Counter
from collections.abc import Callable
from dataclasses import dataclass
from pathlib import Path
from textwrap import indent
import uharfbuzz as hb
import yaml
from fontTools import unicodedata
from data import mongolian
from ... | font-tooling/scripting/real_text_test.py | from __future__ import annotations
import difflib
import re
from collections import Counter
from collections.abc import Callable
from dataclasses import dataclass
from pathlib import Path
from textwrap import indent
import uharfbuzz as hb
import yaml
from fontTools import unicodedata
from data import mongolian
from ... | 0.643105 | 0.15511 |
class MediatorInterface:
"""
The Mediator interface declares a method used by components
to notify the mediator about various events. The mediator
may react to these events and pass the execution to other
components.
"""
def notify(self, sender: object, event: str) -> None:
raise No... | behavioral/mediator/refactoring-guru.py | class MediatorInterface:
"""
The Mediator interface declares a method used by components
to notify the mediator about various events. The mediator
may react to these events and pass the execution to other
components.
"""
def notify(self, sender: object, event: str) -> None:
raise No... | 0.643553 | 0.498474 |
import traceback
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.forms import UserCreationForm, UserChangeForm
from django.template.response import TemplateResponse
from django.conf import settings
from django import http
from django.contrib import messages
fro... | conference/profiles/admin.py | import traceback
from django.contrib import admin
from django.contrib.auth.admin import UserAdmin
from django.contrib.auth.forms import UserCreationForm, UserChangeForm
from django.template.response import TemplateResponse
from django.conf import settings
from django import http
from django.contrib import messages
fro... | 0.347537 | 0.053899 |
import os
import logging
import time
log = logging.getLogger(__name__)
def batch_dl2_to_sensitivity(
dl2_directory,
offset_gammas,
job_ids_from_dl1_dl2,
log_from_dl1_dl2,
source_env,
prod_id,
):
"""
Batches the dl2_to_sensitivity stage (`stages.script_dl2_to_sensitivity` based in th... | lstmcpipe/stages/mc_dl2_to_sensitivity.py |
import os
import logging
import time
log = logging.getLogger(__name__)
def batch_dl2_to_sensitivity(
dl2_directory,
offset_gammas,
job_ids_from_dl1_dl2,
log_from_dl1_dl2,
source_env,
prod_id,
):
"""
Batches the dl2_to_sensitivity stage (`stages.script_dl2_to_sensitivity` based in th... | 0.781497 | 0.311178 |
import requests
class Behaviour:
"""
This class offers methods to manage a behaviours
``from datavillage_sdk.user.behaviour import Behaviour``
``behaviour = Behaviour()``
"""
def __init__(self):
super().__init__()
def create_behaviour(
self, user, consent_receipt_proce... | datavillage_sdk/user/behaviour.py | import requests
class Behaviour:
"""
This class offers methods to manage a behaviours
``from datavillage_sdk.user.behaviour import Behaviour``
``behaviour = Behaviour()``
"""
def __init__(self):
super().__init__()
def create_behaviour(
self, user, consent_receipt_proce... | 0.799794 | 0.333802 |
import json
import string
import sys
class Definition(object):
__tsdoc_weight__ = -100
# Classes
DEF_UNKNOWN = -1
DEF_DESCRIPTION = 0
DEF_VARIABLE = 1
DEF_CONSTANT = 2
DEF_FUNCTION = 3
DEF_TYPE = 4
def_class = DEF_UNKNOWN
def __init__(self):
self.code = '... | tsdoc/tsdoc/__init__.py | import json
import string
import sys
class Definition(object):
__tsdoc_weight__ = -100
# Classes
DEF_UNKNOWN = -1
DEF_DESCRIPTION = 0
DEF_VARIABLE = 1
DEF_CONSTANT = 2
DEF_FUNCTION = 3
DEF_TYPE = 4
def_class = DEF_UNKNOWN
def __init__(self):
self.code = '... | 0.301362 | 0.076064 |
from string import Template
from PyPDF2 import PdfFileReader, PdfFileWriter
def __reorganization(
source_path: str,
target_path: str,
pages=list[int],
):
"""
重组 PDF 的 IO 操作函数, 将 pages 中的所有页面数(合法) 拆分为一个新的PDF.
Args:
source_path (str): 源文件路径
target_path (str): 目标文件路径
pag... | tools/pdf/split_pdf.py | from string import Template
from PyPDF2 import PdfFileReader, PdfFileWriter
def __reorganization(
source_path: str,
target_path: str,
pages=list[int],
):
"""
重组 PDF 的 IO 操作函数, 将 pages 中的所有页面数(合法) 拆分为一个新的PDF.
Args:
source_path (str): 源文件路径
target_path (str): 目标文件路径
pag... | 0.441191 | 0.182407 |
def call(self, inputs, states, training=None): # use implementation=1
h_tm1 = states[0] # previous memory
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
h_tm1, training, count=3)
if self.use_bias:
if not self.... | repo_files/to add/GRU.py | def call(self, inputs, states, training=None): # use implementation=1
h_tm1 = states[0] # previous memory
dp_mask = self.get_dropout_mask_for_cell(inputs, training, count=3)
rec_dp_mask = self.get_recurrent_dropout_mask_for_cell(
h_tm1, training, count=3)
if self.use_bias:
if not self.... | 0.557845 | 0.4831 |
import numpy as np
from scipy.optimize import linear_sum_assignment
from collections import defaultdict
from utils.utils import parse_camera_param
def global2pixel(person_coords, camera_id, camera_param_dict):
# det : X Y Z
world_coord = person_coords / camera_param_dict['discretization_factor'] + camera_param... | track1to2/track1to2_track.py | import numpy as np
from scipy.optimize import linear_sum_assignment
from collections import defaultdict
from utils.utils import parse_camera_param
def global2pixel(person_coords, camera_id, camera_param_dict):
# det : X Y Z
world_coord = person_coords / camera_param_dict['discretization_factor'] + camera_param... | 0.663669 | 0.553324 |
import logging
import os
import uuid
from flask import Flask, render_template, request, send_from_directory
from flask_assets import Environment
from pyhocon import ConfigTree
from search_ui import ClientError, SearchEngine
from search_ui.util import PreferredMime
logger = logging.getLogger('search-ui')
# flask web... | search_ui/app.py | import logging
import os
import uuid
from flask import Flask, render_template, request, send_from_directory
from flask_assets import Environment
from pyhocon import ConfigTree
from search_ui import ClientError, SearchEngine
from search_ui.util import PreferredMime
logger = logging.getLogger('search-ui')
# flask web... | 0.42477 | 0.059976 |
import os
import logging
from google.cloud import vision
from ..storage import StoragePrefix, StorageFile
from . import utils
logger = logging.getLogger(__name__)
class PDFAnalyzer:
# How many pages should be grouped into each json output file.
BATCH_SIZE = 100
# Supported mime_types are: 'application/pd... | gcp/vision.py | import os
import logging
from google.cloud import vision
from ..storage import StoragePrefix, StorageFile
from . import utils
logger = logging.getLogger(__name__)
class PDFAnalyzer:
# How many pages should be grouped into each json output file.
BATCH_SIZE = 100
# Supported mime_types are: 'application/pd... | 0.42656 | 0.085061 |
class Node:
def __init__(self,val,parent=None,rnk=0):
self.Value=val
self.Parent=parent
self.child=0
self.Rank=rnk
self.TotalChildrens=0
self.childNodes=[]
def AddMultiLayer(self,values):
n=len(values)
node=self.addNode(values[0])
if n==1:
return
else:
node.AddMultiLayer(values[1:])
self.r... | Tree.py | class Node:
def __init__(self,val,parent=None,rnk=0):
self.Value=val
self.Parent=parent
self.child=0
self.Rank=rnk
self.TotalChildrens=0
self.childNodes=[]
def AddMultiLayer(self,values):
n=len(values)
node=self.addNode(values[0])
if n==1:
return
else:
node.AddMultiLayer(values[1:])
self.r... | 0.125259 | 0.18591 |
import subprocess
def sort(input_):
"""Invoke sorter on `lines`, return sorted lines."""
process = subprocess.Popen(
['./mesos_include_sorter.py'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
output = process.communicate(input=input_.encode())[0]
return output.decode()
d... | test_mesos_include_sorter.py | import subprocess
def sort(input_):
"""Invoke sorter on `lines`, return sorted lines."""
process = subprocess.Popen(
['./mesos_include_sorter.py'],
stdin=subprocess.PIPE,
stdout=subprocess.PIPE)
output = process.communicate(input=input_.encode())[0]
return output.decode()
d... | 0.788909 | 0.319009 |
from . import api
import argparse
import os.path
import sys
parsing_errors = False
def is_valid_file(parser, arg):
"""Check if the file exists and return its path. Otherwise raise error."""
if not arg: return arg
if not os.path.exists(arg):
parser.error("The file %s does not exist!" % arg)
els... | dagpy/__main__.py | from . import api
import argparse
import os.path
import sys
parsing_errors = False
def is_valid_file(parser, arg):
"""Check if the file exists and return its path. Otherwise raise error."""
if not arg: return arg
if not os.path.exists(arg):
parser.error("The file %s does not exist!" % arg)
els... | 0.350866 | 0.15109 |
import random
import pickle
import os
def print_board(slots, board_size):
st = " "
for i in range(board_size):
st = st + " " + str(i + 1)
print(st)
for row in range(board_size):
st = " "
if row == 0:
for col in range(board_size):
... | minesweeper.py | import random
import pickle
import os
def print_board(slots, board_size):
st = " "
for i in range(board_size):
st = st + " " + str(i + 1)
print(st)
for row in range(board_size):
st = " "
if row == 0:
for col in range(board_size):
... | 0.269999 | 0.318578 |
from unittest import TestCase
from omnicanvas.graphics import ShapeGraphic, BoxGraphic
class BoxGraphicCreationTests(TestCase):
def test_can_create_box_graphic(self):
box = BoxGraphic(10, 20, 100, 200)
self.assertIsInstance(box, ShapeGraphic)
self.assertEqual(box._x, 10)
self.asser... | tests/test_boxes.py | from unittest import TestCase
from omnicanvas.graphics import ShapeGraphic, BoxGraphic
class BoxGraphicCreationTests(TestCase):
def test_can_create_box_graphic(self):
box = BoxGraphic(10, 20, 100, 200)
self.assertIsInstance(box, ShapeGraphic)
self.assertEqual(box._x, 10)
self.asser... | 0.585694 | 0.647004 |
from _TFL import TFL
from _MOM import MOM
from _MOM._Attr.Type import *
from _MOM._Attr import Attr
from _MOM._Pred import Pred
import _MOM._Meta.M_Link
import _MOM.Entity
_Ancestor_Essence = MOM.Id_Entity
class _MOM_Link_ \
(_Ancestor_Essence, metaclass = MOM.Meta.M_Link)... | _MOM/Link.py |
from _TFL import TFL
from _MOM import MOM
from _MOM._Attr.Type import *
from _MOM._Attr import Attr
from _MOM._Pred import Pred
import _MOM._Meta.M_Link
import _MOM.Entity
_Ancestor_Essence = MOM.Id_Entity
class _MOM_Link_ \
(_Ancestor_Essence, metaclass = MOM.Meta.M_Link)... | 0.773559 | 0.171338 |
import os
import time
import cfg.glob
import db.dml
import pypandoc
import utils
# -----------------------------------------------------------------------------
# Global variables.
# -----------------------------------------------------------------------------
PANDOC_PDF_ENGINE_LULATEX: str = "lulatex"
PANDOC_PDF_ENG... | src/dcr/pp/pandoc_dcr.py | import os
import time
import cfg.glob
import db.dml
import pypandoc
import utils
# -----------------------------------------------------------------------------
# Global variables.
# -----------------------------------------------------------------------------
PANDOC_PDF_ENGINE_LULATEX: str = "lulatex"
PANDOC_PDF_ENG... | 0.394201 | 0.174833 |
from email.MIMEText import MIMEText
import logging
import os
import re
import smtplib
import sys
import urllib
import pyauto_functional
import pyauto
sys.path.append(os.path.join(pyauto.PyUITest.DataDir(), 'pyauto_private',
'chromeos', 'network'))
from gsm_sim_info import SIM, PROVIDER_TXT_SERVER
c... | chrome/test/functional/chromeos_txt_msg_functional.py |
from email.MIMEText import MIMEText
import logging
import os
import re
import smtplib
import sys
import urllib
import pyauto_functional
import pyauto
sys.path.append(os.path.join(pyauto.PyUITest.DataDir(), 'pyauto_private',
'chromeos', 'network'))
from gsm_sim_info import SIM, PROVIDER_TXT_SERVER
c... | 0.511717 | 0.105625 |