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
import unittest
import player
TWO_PAIR_HAND = [
{
"rank": "A",
"suit": "hearts"
},
{
"rank": "A",
"suit": "spades"
},
{
"rank": "8",
"suit": "hearts"
},
{
"rank": "8",
"suit": "spades"
},
{
"rank": ... | tests.py | import json
import unittest
import player
TWO_PAIR_HAND = [
{
"rank": "A",
"suit": "hearts"
},
{
"rank": "A",
"suit": "spades"
},
{
"rank": "8",
"suit": "hearts"
},
{
"rank": "8",
"suit": "spades"
},
{
"rank": ... | 0.386185 | 0.353149 |
def _js_devserver(ctx):
files = depset()
for d in ctx.attr.deps:
if hasattr(d, "node_sources"):
files = depset(transitive=[d.node_sources, files])
elif hasattr(d, "files"):
files = depset(transitive=[d.files, files])
if ctx.label.workspace_root:
# We need the... | experimental/js_devserver/defs.bzl | def _js_devserver(ctx):
files = depset()
for d in ctx.attr.deps:
if hasattr(d, "node_sources"):
files = depset(transitive=[d.node_sources, files])
elif hasattr(d, "files"):
files = depset(transitive=[d.files, files])
if ctx.label.workspace_root:
# We need the... | 0.331012 | 0.23156 |
import argparse
import cv2
import numpy as np
import timm
import torch
from PIL import Image
from torchvision import transforms
from vit_grad_rollout import VITAttentionGradRollout
from vit_rollout import VITAttentionRollout
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--use_cuda'... | vit_explain.py | import argparse
import cv2
import numpy as np
import timm
import torch
from PIL import Image
from torchvision import transforms
from vit_grad_rollout import VITAttentionGradRollout
from vit_rollout import VITAttentionRollout
def get_args():
parser = argparse.ArgumentParser()
parser.add_argument('--use_cuda'... | 0.779993 | 0.209308 |
import sys
import inspect
from django import forms
from django.apps import apps
from backend.models import Artist
from backend.models import Album
def get_model_form_class(app_label, model_name):
model_form_class = None
# List all ModelForms in this module
model_forms = [
klass
for name,... | sample_project/frontend/forms.py | import sys
import inspect
from django import forms
from django.apps import apps
from backend.models import Artist
from backend.models import Album
def get_model_form_class(app_label, model_name):
model_form_class = None
# List all ModelForms in this module
model_forms = [
klass
for name,... | 0.342462 | 0.175573 |
import torch
from transformers import BertTokenizer
import transformers
transformers.logging.set_verbosity_error()
import pickle
import argparse
import os
from data.data_loader import load_dataset
from models.bert_multi_tokens import MultiTokensBERT
from prompts.prompt_scorer import ContentPromptScorer, LogicalPrompt... | main.py | import torch
from transformers import BertTokenizer
import transformers
transformers.logging.set_verbosity_error()
import pickle
import argparse
import os
from data.data_loader import load_dataset
from models.bert_multi_tokens import MultiTokensBERT
from prompts.prompt_scorer import ContentPromptScorer, LogicalPrompt... | 0.581303 | 0.173201 |
import os
import yaml
import logging.config
import logging
from pathlib import Path
from email.mime.text import MIMEText
from email.header import Header
from email.utils import formataddr
__all__ = ["setup_logging", "green", "blue", "yellow", "red", "blue_bar"]
class TColors:
"""
Terminal Colors
"""
... | fx_lib/log.py | import os
import yaml
import logging.config
import logging
from pathlib import Path
from email.mime.text import MIMEText
from email.header import Header
from email.utils import formataddr
__all__ = ["setup_logging", "green", "blue", "yellow", "red", "blue_bar"]
class TColors:
"""
Terminal Colors
"""
... | 0.361616 | 0.08819 |
import os
import numpy as np
import pandas as pd
def sid_subdir_path(sid):
"""
Format subdir path to limit the number directories in any given
subdirectory to 100.
The number in each directory is designed to support at least 100000
equities.
Parameters
----------
sid : int
A... | rqalpha/mod/rqalpha_mod_fxdayu_source/share/utils.py | import os
import numpy as np
import pandas as pd
def sid_subdir_path(sid):
"""
Format subdir path to limit the number directories in any given
subdirectory to 100.
The number in each directory is designed to support at least 100000
equities.
Parameters
----------
sid : int
A... | 0.716913 | 0.441673 |
import unittest, sys, os, os.path
curfilePath = os.path.abspath(__file__)
curDir = os.path.abspath(os.path.join(curfilePath,os.pardir)) # this will return current directory in which python file resides.
parentDir = os.path.abspath(os.path.join(curDir,os.pardir)) # this will return parent directory.
sys.path.append(pare... | test_files/combine_test.py | import unittest, sys, os, os.path
curfilePath = os.path.abspath(__file__)
curDir = os.path.abspath(os.path.join(curfilePath,os.pardir)) # this will return current directory in which python file resides.
parentDir = os.path.abspath(os.path.join(curDir,os.pardir)) # this will return parent directory.
sys.path.append(pare... | 0.251372 | 0.32338 |
from datetime import datetime
from dateutil.relativedelta import relativedelta
from openerp.osv import fields, osv
import openerp.addons.decimal_precision as dp
import time
from openerp.tools.translate import _
import datetime
import simplejson
try:
from openerp import SUPERUSER_ID
except ImportError:
SUPERUS... | Code/odooerp/odoo-8.0/adding/purchase_order/purchase_order.py |
from datetime import datetime
from dateutil.relativedelta import relativedelta
from openerp.osv import fields, osv
import openerp.addons.decimal_precision as dp
import time
from openerp.tools.translate import _
import datetime
import simplejson
try:
from openerp import SUPERUSER_ID
except ImportError:
SUPERUS... | 0.403802 | 0.11737 |
import numpy as np
import numba
import pyfftw
from scipy import ndimage as scnd
from ..proc import sobel_canny as sc
from ..util import gauss_utils as gt
from ..util import image_utils as iu
@numba.jit
def resize_rotate(original_4D,
final_size,
rotangle,
sampler=2,... | pty/pie.py | import numpy as np
import numba
import pyfftw
from scipy import ndimage as scnd
from ..proc import sobel_canny as sc
from ..util import gauss_utils as gt
from ..util import image_utils as iu
@numba.jit
def resize_rotate(original_4D,
final_size,
rotangle,
sampler=2,... | 0.749912 | 0.640495 |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
from sklearn.compose import ColumnTransformer, make_column_tran... | src/preprocessor.py |
import pandas as pd
import numpy as np
from sklearn.model_selection import train_test_split
from sklearn.pipeline import make_pipeline
from sklearn.impute import SimpleImputer
from sklearn.preprocessing import OneHotEncoder, OrdinalEncoder, StandardScaler
from sklearn.compose import ColumnTransformer, make_column_tran... | 0.739234 | 0.305503 |
from json import load
def gen_mods():
with open(r'C:\git\RePoE\RePoE\data\mods.json', 'r') as f:
mods = load(f)
with open(r'C:\git\RePoE\RePoE\data\stat_translations.json', 'r') as f:
stats = load(f)
map_mods = set()
for mod in mods:
if mods[mod]['generation_type'] in ['prefix', 'suffix'] and (any(x['weigh... | helper_files/gen_map_mods.py | from json import load
def gen_mods():
with open(r'C:\git\RePoE\RePoE\data\mods.json', 'r') as f:
mods = load(f)
with open(r'C:\git\RePoE\RePoE\data\stat_translations.json', 'r') as f:
stats = load(f)
map_mods = set()
for mod in mods:
if mods[mod]['generation_type'] in ['prefix', 'suffix'] and (any(x['weigh... | 0.217504 | 0.257345 |
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from swagger_server.models.base_model_ import Model
from swagger_server import util
class Germplasm(Model):
"""NOTE: This class is auto generated by the swagger code generator pr... | swagger_codegen_server/swagger_server/models/germplasm.py |
from __future__ import absolute_import
from datetime import date, datetime # noqa: F401
from typing import List, Dict # noqa: F401
from swagger_server.models.base_model_ import Model
from swagger_server import util
class Germplasm(Model):
"""NOTE: This class is auto generated by the swagger code generator pr... | 0.803945 | 0.078254 |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import taggit.managers
class Migration(migrations.Migration):
dependencies = [
('taggit', '0002_auto_20150616_2121'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('ar... | mysite/article/migrations/0002_auto_20190306_1640.py |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
import taggit.managers
class Migration(migrations.Migration):
dependencies = [
('taggit', '0002_auto_20150616_2121'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
('ar... | 0.524151 | 0.092852 |
from concurrent.futures import ThreadPoolExecutor
import typing
from unittest import mock
from common.bounded_executor import BoundedExecutor
def create_bounded_pool_executor(
bound, max_workers, block_timeout
) -> typing.Tuple[mock.MagicMock, BoundedExecutor]:
"""Creates a mocked thread pool and a bounded ... | common/tests/test_bounded_executor.py |
from concurrent.futures import ThreadPoolExecutor
import typing
from unittest import mock
from common.bounded_executor import BoundedExecutor
def create_bounded_pool_executor(
bound, max_workers, block_timeout
) -> typing.Tuple[mock.MagicMock, BoundedExecutor]:
"""Creates a mocked thread pool and a bounded ... | 0.860164 | 0.49939 |
import pickle
from functools import lru_cache
from urllib.parse import urlparse
import numpy as np
from gensim.models.keyedvectors import KeyedVectors
from redis import Redis
from redis.exceptions import RedisError
from tqdm import tqdm
class RedisKeyedVectorException(Exception):
""" Raised when RedisKeyedVector... | textpipe/wrappers.py | import pickle
from functools import lru_cache
from urllib.parse import urlparse
import numpy as np
from gensim.models.keyedvectors import KeyedVectors
from redis import Redis
from redis.exceptions import RedisError
from tqdm import tqdm
class RedisKeyedVectorException(Exception):
""" Raised when RedisKeyedVector... | 0.643777 | 0.427815 |
from SPARQLWrapper import SPARQLWrapper, JSON, POST, URLENCODED
import memcache
import logging
import hashlib
import pickle
import pandas as pd
import datetime
import typing
import os
import json
from datamart_isi import config
from datamart_isi.utilities import connection
from datamart_isi.utilities.singleton import s... | datamart_isi/cache/general_search_cache.py | from SPARQLWrapper import SPARQLWrapper, JSON, POST, URLENCODED
import memcache
import logging
import hashlib
import pickle
import pandas as pd
import datetime
import typing
import os
import json
from datamart_isi import config
from datamart_isi.utilities import connection
from datamart_isi.utilities.singleton import s... | 0.568416 | 0.062991 |
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from googlecloudsdk.calliope.concepts import concepts
from googlecloudsdk.calliope.concepts import deps
from googlecloudsdk.command_lib.util.concepts import concept_parsers
from googlecloudsdk.core import pro... | lib/googlecloudsdk/command_lib/transcoder/resource_args.py | from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from googlecloudsdk.calliope.concepts import concepts
from googlecloudsdk.calliope.concepts import deps
from googlecloudsdk.command_lib.util.concepts import concept_parsers
from googlecloudsdk.core import pro... | 0.690976 | 0.061763 |
import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_hub as hub
import pandas as pd
# Keras
from tensorflow import keras
import keras.layers as layers
from keras.models import Model
from keras.utils import plot_model
from keras import backend as K
# General imports
import gzip
... | Tensorflow/Amazon Question Answer model/Model.py | import tensorflow as tf
import numpy as np
import matplotlib.pyplot as plt
import tensorflow_hub as hub
import pandas as pd
# Keras
from tensorflow import keras
import keras.layers as layers
from keras.models import Model
from keras.utils import plot_model
from keras import backend as K
# General imports
import gzip
... | 0.393968 | 0.330958 |
from operator import itemgetter
from typing import List, Union
from pylatex import Alignat, NoEscape, LongTable, MultiColumn
try:
from pytexit import py2tex
except ModuleNotFoundError:
pass
def py2tex_modified(exps: Union[List, tuple, str]):
if isinstance(exps, tuple):
exps = list(exps)
if ... | fsetools/etc/latex.py | from operator import itemgetter
from typing import List, Union
from pylatex import Alignat, NoEscape, LongTable, MultiColumn
try:
from pytexit import py2tex
except ModuleNotFoundError:
pass
def py2tex_modified(exps: Union[List, tuple, str]):
if isinstance(exps, tuple):
exps = list(exps)
if ... | 0.550245 | 0.287658 |
import subprocess
import re
import os
from identity_matching.src.matching import IdentityMatcher
from identity_matching.src.model import DistanceModel
from identity_matching.src.preprocessor import DistancePreprocessor
from identity_matching.src.transform_shortlog import process_shortlog_line
def match_emails(direct... | src/identity_matching/matcher.py | import subprocess
import re
import os
from identity_matching.src.matching import IdentityMatcher
from identity_matching.src.model import DistanceModel
from identity_matching.src.preprocessor import DistancePreprocessor
from identity_matching.src.transform_shortlog import process_shortlog_line
def match_emails(direct... | 0.152568 | 0.081849 |
import importlib
import json
import logging
import os
import sys
import cherrypy
from cryptojwt.key_jar import build_keyjar
from cryptojwt.key_jar import KeyJar
from oidcrp import RPHandler
logger = logging.getLogger("")
LOGFILE_NAME = 'farp.log'
hdlr = logging.FileHandler(LOGFILE_NAME)
base_formatter = logging.For... | example/cherrypy_rp/chrp/rp.py | import importlib
import json
import logging
import os
import sys
import cherrypy
from cryptojwt.key_jar import build_keyjar
from cryptojwt.key_jar import KeyJar
from oidcrp import RPHandler
logger = logging.getLogger("")
LOGFILE_NAME = 'farp.log'
hdlr = logging.FileHandler(LOGFILE_NAME)
base_formatter = logging.For... | 0.18743 | 0.043447 |
import matplotlib
import matplotlib.pyplot as plt
import os, sys
import numpy as np
import string
import math
import operator
import multiprocessing
from multiprocessing import Pool
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.size'] ... | examples/4-4-1-reconstruct_eclipse/plot.py | import matplotlib
import matplotlib.pyplot as plt
import os, sys
import numpy as np
import string
import math
import operator
import multiprocessing
from multiprocessing import Pool
matplotlib.rcParams['text.usetex'] = True
matplotlib.rcParams['font.family'] = 'serif'
matplotlib.rcParams['font.size'] ... | 0.273186 | 0.284124 |
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
from frappe.utils.data import now
from mvd.mvd.doctype.mitgliedschaft.mitgliedschaft import create_mitgliedschaftsrechnung
from frappe.utils.background_jobs import enqueue
import time
from frappe.utils.csvutils import to_c... | mvd/mvd/doctype/mv_jahresversand/mv_jahresversand.py |
from __future__ import unicode_literals
import frappe
from frappe.model.document import Document
from frappe.utils.data import now
from mvd.mvd.doctype.mitgliedschaft.mitgliedschaft import create_mitgliedschaftsrechnung
from frappe.utils.background_jobs import enqueue
import time
from frappe.utils.csvutils import to_c... | 0.208018 | 0.156459 |
import sys, json, os, re, requests
import nvd, scancode
from glob import glob
from os import path, remove
from subprocess import call
from collections import OrderedDict
from shutil import rmtree
def main(argv):
if len(argv) <= 1:
print("Usage: ./riskmetrics.py <github-repo-url>")
exit(2)
... | riskmetrics/riskmetrics.py |
import sys, json, os, re, requests
import nvd, scancode
from glob import glob
from os import path, remove
from subprocess import call
from collections import OrderedDict
from shutil import rmtree
def main(argv):
if len(argv) <= 1:
print("Usage: ./riskmetrics.py <github-repo-url>")
exit(2)
... | 0.381104 | 0.123762 |
# Configuration files consist of functions which define build "targets."
# This function creates a Python executable and installs it in a destination
# directory.
def resource_callback(policy, resource):
if type(resource) in ("PythonExtensionModule") or "pytest" in resource.name or "pluggy" in resource.name or "... | pyoxidizer.bzl |
# Configuration files consist of functions which define build "targets."
# This function creates a Python executable and installs it in a destination
# directory.
def resource_callback(policy, resource):
if type(resource) in ("PythonExtensionModule") or "pytest" in resource.name or "pluggy" in resource.name or "... | 0.61682 | 0.221025 |
import logging
import numpy as np
from deprecated import deprecated
logger = logging.getLogger(__name__)
@deprecated(version="1.1")
def index(arr, item): # pragma: no cover
for idx, val in np.ndenumerate(arr):
if val == item:
return idx
# If no item was found return None, other return t... | omicron/core/accelerate.py | import logging
import numpy as np
from deprecated import deprecated
logger = logging.getLogger(__name__)
@deprecated(version="1.1")
def index(arr, item): # pragma: no cover
for idx, val in np.ndenumerate(arr):
if val == item:
return idx
# If no item was found return None, other return t... | 0.634543 | 0.353191 |
from django.db.models import Avg, Sum, Count
from django import forms
from django.forms.widgets import Input
from django.http import HttpResponse
from django.template import RequestContext, loader
from django.views.decorators.cache import cache_page
from colourlens.models import Artwork, Colour
PROM_ATTRS = {'min': '0... | colourlens/views.py | from django.db.models import Avg, Sum, Count
from django import forms
from django.forms.widgets import Input
from django.http import HttpResponse
from django.template import RequestContext, loader
from django.views.decorators.cache import cache_page
from colourlens.models import Artwork, Colour
PROM_ATTRS = {'min': '0... | 0.705684 | 0.093761 |
import collections
import copy
import progressbar
from . import mutator_options
from . import semantics
Mutation = collections.namedtuple('Mutation', ['nodeid', 'name', 'localm', 'globalm'])
enabled_mutators = []
def collect_mutators(args):
"""Initializes the list of all active mutators."""
global enabled_m... | pydelta/mutator.py | import collections
import copy
import progressbar
from . import mutator_options
from . import semantics
Mutation = collections.namedtuple('Mutation', ['nodeid', 'name', 'localm', 'globalm'])
enabled_mutators = []
def collect_mutators(args):
"""Initializes the list of all active mutators."""
global enabled_m... | 0.681727 | 0.220762 |
import requests
from datetime import datetime, timedelta
from requests.auth import HTTPBasicAuth
from requests import Request
from .config import logger, \
GPM_APP_PASSWORD, \
GPM_EMAIL_ADDRESS, \
SPOTIFY_CLIENT_ID, \
SPOTIFY_CLIENT_SECRET
from .exceptions import AuthError
from .utils import _raise_fo... | musicsync/auth.py | import requests
from datetime import datetime, timedelta
from requests.auth import HTTPBasicAuth
from requests import Request
from .config import logger, \
GPM_APP_PASSWORD, \
GPM_EMAIL_ADDRESS, \
SPOTIFY_CLIENT_ID, \
SPOTIFY_CLIENT_SECRET
from .exceptions import AuthError
from .utils import _raise_fo... | 0.474875 | 0.054099 |
from fastapi.testclient import TestClient
from api.main import app, get_db
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from api.database import Base
import os
SQLALCHEMY_DATABASE_PATH = "./test.db"
SQLALCHEMY_DATABASE_URL = "sqlite:///" + SQLALCHEMY_DATABASE_PATH
try:
os.remove(SQL... | tests/test_1.py | from fastapi.testclient import TestClient
from api.main import app, get_db
from sqlalchemy import create_engine
from sqlalchemy.orm import sessionmaker
from api.database import Base
import os
SQLALCHEMY_DATABASE_PATH = "./test.db"
SQLALCHEMY_DATABASE_URL = "sqlite:///" + SQLALCHEMY_DATABASE_PATH
try:
os.remove(SQL... | 0.632162 | 0.349894 |
from deeppavlov.models.tokenizers.spacy_tokenizer import StreamSpacyTokenizer
from deeppavlov.models.sklearn import SklearnComponent
from deeppavlov.dataset_readers.faq_reader import FaqDatasetReader
from deeppavlov.core.data.data_learning_iterator import DataLearningIterator
FAQ_DATASET_URL = "../data/faq.csv"
"""
... | heroku_client/echobot.py | from deeppavlov.models.tokenizers.spacy_tokenizer import StreamSpacyTokenizer
from deeppavlov.models.sklearn import SklearnComponent
from deeppavlov.dataset_readers.faq_reader import FaqDatasetReader
from deeppavlov.core.data.data_learning_iterator import DataLearningIterator
FAQ_DATASET_URL = "../data/faq.csv"
"""
... | 0.655557 | 0.379896 |
from solutionparser import SolutionParser, SladerBook, SladerAnswer, SolutionParameters
import xml.etree.ElementTree as ET
# Alias for SolutionParameters
Params = SolutionParameters
class XmlSolutionParser(SolutionParser):
""" Parse solutions from a given XML file. See example file
for the format
"""... | src/xmlparser.py | from solutionparser import SolutionParser, SladerBook, SladerAnswer, SolutionParameters
import xml.etree.ElementTree as ET
# Alias for SolutionParameters
Params = SolutionParameters
class XmlSolutionParser(SolutionParser):
""" Parse solutions from a given XML file. See example file
for the format
"""... | 0.68637 | 0.273405 |
from math import *
# --------------------------------------
# --
# -- PROBLEME I
# --
# --------------------------------------
def lucas1(n):
if n == 0:
return 2
phi = (1 + sqrt(5)) / 2
phi2 = (1 - sqrt(5)) / 2
return phi ** n + phi2 ** n
def lucas2(n):
if n == 0:
return 2
... | exo_6_1.py | from math import *
# --------------------------------------
# --
# -- PROBLEME I
# --
# --------------------------------------
def lucas1(n):
if n == 0:
return 2
phi = (1 + sqrt(5)) / 2
phi2 = (1 - sqrt(5)) / 2
return phi ** n + phi2 ** n
def lucas2(n):
if n == 0:
return 2
... | 0.085327 | 0.499634 |
# %%
from utils import (
get_cifar10_data_loaders,
compute_validation_accuracy_multi,
NN_FC_CrossEntropy,
train_one_epoch,
)
from fastprogress.fastprogress import master_bar
import torch
from torch import nn
from torchvision.utils import make_grid
from torchvision.models import resnet18
import matpl... | lectures/l21-convolutional-neural-networks.py |
# %%
from utils import (
get_cifar10_data_loaders,
compute_validation_accuracy_multi,
NN_FC_CrossEntropy,
train_one_epoch,
)
from fastprogress.fastprogress import master_bar
import torch
from torch import nn
from torchvision.utils import make_grid
from torchvision.models import resnet18
import matpl... | 0.86332 | 0.551393 |
import pydicom
import os
import numpy as np
from skimage.morphology import disk, binary_dilation
from cyvlfeat.fisher import fisher
from sklearn.linear_model import LogisticRegression
from numba import cuda
import pickle
class Classifier:
def __init__(self, fileData, rawDataPath, maskDataPath):
self.PATH... | server/classifier.py | import pydicom
import os
import numpy as np
from skimage.morphology import disk, binary_dilation
from cyvlfeat.fisher import fisher
from sklearn.linear_model import LogisticRegression
from numba import cuda
import pickle
class Classifier:
def __init__(self, fileData, rawDataPath, maskDataPath):
self.PATH... | 0.238994 | 0.209551 |
import json
import os
import pickle
import pandas as pd
def append_csv(data, path):
""" append to a csv """
assert path.endswith(".csv")
to_log = data if isinstance(data, pd.DataFrame) else pd.DataFrame(data)
if "~" in path:
print("Dont include the user path as ~!, use os.path.expanduser")
... | doors/inout.py | import json
import os
import pickle
import pandas as pd
def append_csv(data, path):
""" append to a csv """
assert path.endswith(".csv")
to_log = data if isinstance(data, pd.DataFrame) else pd.DataFrame(data)
if "~" in path:
print("Dont include the user path as ~!, use os.path.expanduser")
... | 0.322099 | 0.150403 |
from __future__ import unicode_literals
from .. import Provider as LoremProvider
class Provider(LoremProvider):
word_list = (
'alias', 'consequatur', 'aut', 'perferendis', 'sit', 'voluptatem',
'accusantium', 'doloremque', 'aperiam', 'eaque', 'ipsa', 'quae', 'ab',
'illo', 'inventore', 'ver... | components/faker/faker/providers/lorem/la/__init__.py |
from __future__ import unicode_literals
from .. import Provider as LoremProvider
class Provider(LoremProvider):
word_list = (
'alias', 'consequatur', 'aut', 'perferendis', 'sit', 'voluptatem',
'accusantium', 'doloremque', 'aperiam', 'eaque', 'ipsa', 'quae', 'ab',
'illo', 'inventore', 'ver... | 0.485356 | 0.235779 |
"""Launch experiments through an orchestrator."""
import threading
import time
import datetime
import logging
from .experiment import Experiment
from .dal import DAL
class System(threading.Thread):
def __init__(self, orchestrator, db, kafka_server):
threading.Thread.__init__(self)
self.logger = ... | scheduler/system/system.py | """Launch experiments through an orchestrator."""
import threading
import time
import datetime
import logging
from .experiment import Experiment
from .dal import DAL
class System(threading.Thread):
def __init__(self, orchestrator, db, kafka_server):
threading.Thread.__init__(self)
self.logger = ... | 0.727975 | 0.146209 |
from typing import List
from .comment_tokenizer import scan_for_comment
from .iterator import LineWrapIterator, Span
from .number_tokenizer import scan_for_number
from .quote_tokenizer import scan_for_quote
from .remaining_tokenizer import scan_for_remaining, RemainingToken
from .token import Token, StartToken, EndTok... | src/quom/tokenizer/preprocessor_tokenizer.py | from typing import List
from .comment_tokenizer import scan_for_comment
from .iterator import LineWrapIterator, Span
from .number_tokenizer import scan_for_number
from .quote_tokenizer import scan_for_quote
from .remaining_tokenizer import scan_for_remaining, RemainingToken
from .token import Token, StartToken, EndTok... | 0.7181 | 0.154759 |
import pytest
import sys
import StringIO
from pypy.module.cpyext.state import State
from pypy.module.cpyext.pyobject import make_ref
from pypy.module.cpyext.test.test_api import BaseApiTest
from pypy.module.cpyext.test.test_cpyext import AppTestCpythonExtensionBase
from rpython.rtyper.lltypesystem import rffi
class A... | pypy/module/cpyext/test/test_contextvars.py | import pytest
import sys
import StringIO
from pypy.module.cpyext.state import State
from pypy.module.cpyext.pyobject import make_ref
from pypy.module.cpyext.test.test_api import BaseApiTest
from pypy.module.cpyext.test.test_cpyext import AppTestCpythonExtensionBase
from rpython.rtyper.lltypesystem import rffi
class A... | 0.270769 | 0.162314 |
import os
import pickle
import random
import logging
from collections import Counter
import xmltodict
import numpy as np
import torch
import sentencepiece as spm
from matplotlib import pyplot as plt
from torch.utils.data import Dataset, DataLoader
from transformers import T5Tokenizer
def prepare_data(file_name):
... | assin/assin_dataset.py | import os
import pickle
import random
import logging
from collections import Counter
import xmltodict
import numpy as np
import torch
import sentencepiece as spm
from matplotlib import pyplot as plt
from torch.utils.data import Dataset, DataLoader
from transformers import T5Tokenizer
def prepare_data(file_name):
... | 0.602997 | 0.255364 |
import os
import numpy as np
from statistics import mean, stdev
from nnga.datasets.base_dataset import BaseDataset
from nnga.utils.data_io import (
load_csv_file,
load_csv_line,
save_scale_parameters,
save_encoder_parameters,
save_decoder_parameters,
)
from nnga.utils.data_manipulation import scale_... | nnga/datasets/classification/csv_dataset.py | import os
import numpy as np
from statistics import mean, stdev
from nnga.datasets.base_dataset import BaseDataset
from nnga.utils.data_io import (
load_csv_file,
load_csv_line,
save_scale_parameters,
save_encoder_parameters,
save_decoder_parameters,
)
from nnga.utils.data_manipulation import scale_... | 0.663124 | 0.340896 |
from __future__ import absolute_import, division, print_function
__metaclass__ = type
from ansible_collections.cisco.nxos.plugins.modules import nxos_static_routes
from ansible_collections.cisco.nxos.tests.unit.compat.mock import (
patch,
MagicMock,
)
from ansible_collections.cisco.nxos.tests.unit.modules.ut... | venv/lib/python3.6/site-packages/ansible_collections/cisco/nxos/tests/unit/modules/network/nxos/test_nxos_static_routes.py |
from __future__ import absolute_import, division, print_function
__metaclass__ = type
from ansible_collections.cisco.nxos.plugins.modules import nxos_static_routes
from ansible_collections.cisco.nxos.tests.unit.compat.mock import (
patch,
MagicMock,
)
from ansible_collections.cisco.nxos.tests.unit.modules.ut... | 0.66454 | 0.085633 |
import copy
import logging
import functools
from pathlib import Path
from joblib import Memory
from typing import Optional, Dict, Tuple, List, Iterable, Union, Callable, Any, Sequence
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
import PIL
import numpy as np
import albumentations
... | src/deepcv/meta/data/preprocess.py | import copy
import logging
import functools
from pathlib import Path
from joblib import Memory
from typing import Optional, Dict, Tuple, List, Iterable, Union, Callable, Any, Sequence
import torch
import torchvision
from torch.utils.data import Dataset, DataLoader
import PIL
import numpy as np
import albumentations
... | 0.904864 | 0.353149 |
from collections import OrderedDict
from django.contrib.auth.models import User
from django.http import Http404
from django.template import loader
from mod import dispatch_module_hook
from .models import Project, ProjectResult, Message, MessageResult, Result
from .search import SearchEngine
from rest_framework import... | api/rest.py |
from collections import OrderedDict
from django.contrib.auth.models import User
from django.http import Http404
from django.template import loader
from mod import dispatch_module_hook
from .models import Project, ProjectResult, Message, MessageResult, Result
from .search import SearchEngine
from rest_framework import... | 0.521715 | 0.106924 |
import csv
import pandas as pd
import warnings
import string
import re
import os
import fasttext
warnings.filterwarnings('ignore')
PRETRAINED_MODEL_PATH = './files/lid.176.ftz'
def language_detection(dataset, clean_data_dir, language_list):
df = pd.read_csv(dataset, sep='\t', header=0)
df = df[['Tweet']]
... | src/language_detection.py | import csv
import pandas as pd
import warnings
import string
import re
import os
import fasttext
warnings.filterwarnings('ignore')
PRETRAINED_MODEL_PATH = './files/lid.176.ftz'
def language_detection(dataset, clean_data_dir, language_list):
df = pd.read_csv(dataset, sep='\t', header=0)
df = df[['Tweet']]
... | 0.192122 | 0.196363 |
import scipy.io as scio
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRe... | test.py | import scipy.io as scio
import numpy as np
from sklearn.cluster import KMeans
from sklearn.metrics import normalized_mutual_info_score, adjusted_rand_score
from sklearn.metrics import f1_score, accuracy_score, roc_auc_score
from sklearn.model_selection import train_test_split
from sklearn.linear_model import LogisticRe... | 0.569733 | 0.330363 |
from typing import Iterable
import torch
import torch.nn as nn
from lgp.models.alfred.voxel_grid import VoxelGrid
from lgp.models.alfred.projection.projection_ops import project_3d_camera_points_to_2d_pixels, project_3d_points, scatter_add_and_pool
from lgp.models.alfred.projection.image_to_pointcloud import ImageToP... | lgp/models/alfred/projection/voxel_mask_to_image_mask.py | from typing import Iterable
import torch
import torch.nn as nn
from lgp.models.alfred.voxel_grid import VoxelGrid
from lgp.models.alfred.projection.projection_ops import project_3d_camera_points_to_2d_pixels, project_3d_points, scatter_add_and_pool
from lgp.models.alfred.projection.image_to_pointcloud import ImageToP... | 0.94528 | 0.446796 |
import nltk
import numpy as np
from collections import Counter
from scipy.sparse import csc_matrix
from sklearn.base import BaseEstimator
from sklearn.feature_extraction.text import VectorizerMixin
from nltk.corpus import sentiwordnet as swn
class NLTKVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a colle... | tests/vectorizers/NLTKVectorizer.py | import nltk
import numpy as np
from collections import Counter
from scipy.sparse import csc_matrix
from sklearn.base import BaseEstimator
from sklearn.feature_extraction.text import VectorizerMixin
from nltk.corpus import sentiwordnet as swn
class NLTKVectorizer(BaseEstimator, VectorizerMixin):
"""Convert a colle... | 0.72526 | 0.363449 |
import gzip
import io
import numpy
import logging
from lxml import etree
from .openquake_gmpe import OpenQuakeGMPE
class ShakeMap(object):
"""ShakeMap reader.
"""
def __init__(self, gmice=None):
"""Constructor.
"""
self.data = None
self.grid = None
self.gmice = gm... | eewperformance/shakemap.py |
import gzip
import io
import numpy
import logging
from lxml import etree
from .openquake_gmpe import OpenQuakeGMPE
class ShakeMap(object):
"""ShakeMap reader.
"""
def __init__(self, gmice=None):
"""Constructor.
"""
self.data = None
self.grid = None
self.gmice = gm... | 0.785514 | 0.294334 |
from enum import Enum
class CharacterType(Enum):
Role = 'Role'
Species = 'Species'
PhysicalAttribute = 'PhysicalAttribute'
MentalAttribute = 'MentalAttribute'
BehaviouralAttribute = 'BehaviouralAttribute'
SkillCapability = 'SkillCapability'
Aesthetic = 'Aesthetic'
class Role(Enum):
C... | story/characters.py | from enum import Enum
class CharacterType(Enum):
Role = 'Role'
Species = 'Species'
PhysicalAttribute = 'PhysicalAttribute'
MentalAttribute = 'MentalAttribute'
BehaviouralAttribute = 'BehaviouralAttribute'
SkillCapability = 'SkillCapability'
Aesthetic = 'Aesthetic'
class Role(Enum):
C... | 0.547464 | 0.15511 |
import json
from operator import add
def get_wire_coordinates(wire):
'''Returns a list of coordinates covered by the wire. A wire is a list of
directions and distances originating from (0, 0).
For example, ['R8','U5','L5','D3'] should return [ (0,0), (1, 0), (2, 0) ...]'''
DIRECTIONS = {
'D': (... | 2019/day_3.py | import json
from operator import add
def get_wire_coordinates(wire):
'''Returns a list of coordinates covered by the wire. A wire is a list of
directions and distances originating from (0, 0).
For example, ['R8','U5','L5','D3'] should return [ (0,0), (1, 0), (2, 0) ...]'''
DIRECTIONS = {
'D': (... | 0.643777 | 0.511229 |
import os, math
import pickle as pk
import numpy as np
from collections.abc import Iterable
def readPLA(fn):
if not os.path.isfile(fn):
print('Warning: PLA "{}" not found.'.format(fn))
return (None,) * 3
getNum = lambda s, head: int(s.strip('\n').replace(head, '').replace(' ', ''))
... | utils.py | import os, math
import pickle as pk
import numpy as np
from collections.abc import Iterable
def readPLA(fn):
if not os.path.isfile(fn):
print('Warning: PLA "{}" not found.'.format(fn))
return (None,) * 3
getNum = lambda s, head: int(s.strip('\n').replace(head, '').replace(' ', ''))
... | 0.196672 | 0.29798 |
from flask import Flask, request
from datetime import datetime
from database import get_pgconn, insert
import os
"""
Simple greeting app that runs on Python and flask. It remembers the names and
left messages, and stores them into a postgres database.
"""
# Determine the port that this app is running ob via environme... | dockerfiles/greeting/0.2/greeting.py | from flask import Flask, request
from datetime import datetime
from database import get_pgconn, insert
import os
"""
Simple greeting app that runs on Python and flask. It remembers the names and
left messages, and stores them into a postgres database.
"""
# Determine the port that this app is running ob via environme... | 0.603581 | 0.203787 |
"""
A test framework for testing DPDK.
"""
import os
import sys
import argparse
# change operation directory
os.chdir("../")
cwd = os.getcwd()
sys.path.append(cwd + '/nics')
sys.path.append(cwd + '/framework')
#sys.path.append(cwd + '/tests') # suites module path should be loaded in dts/run_all, not here
sys.path.app... | ena-dts/framework/main.py | """
A test framework for testing DPDK.
"""
import os
import sys
import argparse
# change operation directory
os.chdir("../")
cwd = os.getcwd()
sys.path.append(cwd + '/nics')
sys.path.append(cwd + '/framework')
#sys.path.append(cwd + '/tests') # suites module path should be loaded in dts/run_all, not here
sys.path.app... | 0.348313 | 0.134236 |
import config
import collections.abc as container_abcs
import errno
import numpy as np
import os
import time
import torch
import torch.nn as nn
import torchvision
from itertools import repeat
from matplotlib import pyplot as plt
from PIL import Image
from torchvision.utils import make_grid
from torchvision.utils import... | src/utils.py | import config
import collections.abc as container_abcs
import errno
import numpy as np
import os
import time
import torch
import torch.nn as nn
import torchvision
from itertools import repeat
from matplotlib import pyplot as plt
from PIL import Image
from torchvision.utils import make_grid
from torchvision.utils import... | 0.299822 | 0.196402 |
import unittest
import time
import serial
from spawn_virtual_devices import spawn_device, get_virtual_ports
from hibike_process import identify_smart_sensors
import hibike_message as hm
class IdentifySmartSensorsTests(unittest.TestCase):
"""
Tests for `identify_smart_sensors`.
"""
VIRTUAL_DEVICE_TYPES ... | hibike/hibike_tests/hibike_process_tests.py | import unittest
import time
import serial
from spawn_virtual_devices import spawn_device, get_virtual_ports
from hibike_process import identify_smart_sensors
import hibike_message as hm
class IdentifySmartSensorsTests(unittest.TestCase):
"""
Tests for `identify_smart_sensors`.
"""
VIRTUAL_DEVICE_TYPES ... | 0.486332 | 0.32826 |
from itertools import chain
from django import forms
from django.contrib.auth import get_user_model
from django.forms import models
from django.forms.fields import MultipleChoiceField
from ..categories.models import Category
from .models import Moderator, ModeratorEvent, ModeratorQuerySet
User = get_user_model()
#... | forum/moderation/forms.py | from itertools import chain
from django import forms
from django.contrib.auth import get_user_model
from django.forms import models
from django.forms.fields import MultipleChoiceField
from ..categories.models import Category
from .models import Moderator, ModeratorEvent, ModeratorQuerySet
User = get_user_model()
#... | 0.461259 | 0.091423 |
import torch
from torch import nn
import tqdm
import numpy as np
from kme.extern.sam import SAM
def compute_accuracy(pred_y, y, mean_reduce=False):
pred = torch.argmax(pred_y, dim=1)
correct = pred.eq(y).sum().float()
if mean_reduce:
return correct.item() / float(len(y))
return correct.item()
... | kme/tools/training.py | import torch
from torch import nn
import tqdm
import numpy as np
from kme.extern.sam import SAM
def compute_accuracy(pred_y, y, mean_reduce=False):
pred = torch.argmax(pred_y, dim=1)
correct = pred.eq(y).sum().float()
if mean_reduce:
return correct.item() / float(len(y))
return correct.item()
... | 0.753104 | 0.455804 |
import os
import numpy as np
import pandas as pd
from tensorflow.keras.utils import to_categorical
from sklearn import model_selection
class ACIC(object):
def __init__(
self,
path,
trial,
center=False,
exclude_population=False,
):
self.trial ... | ucate/library/data/acic.py | import os
import numpy as np
import pandas as pd
from tensorflow.keras.utils import to_categorical
from sklearn import model_selection
class ACIC(object):
def __init__(
self,
path,
trial,
center=False,
exclude_population=False,
):
self.trial ... | 0.556159 | 0.322299 |
import sys
import json
import time
import anchore.anchore_utils
gate_name = "ANCHORESEC"
triggers = {
'VULNLOW':
{
'description':'triggers if a vulnerability of LOW severity is found',
'params':'none'
},
'VULNMEDIUM':
{
'description':'triggers if a vulnerability of MED sev... | anchore/anchore-modules/gates/98_anchoresec.py |
import sys
import json
import time
import anchore.anchore_utils
gate_name = "ANCHORESEC"
triggers = {
'VULNLOW':
{
'description':'triggers if a vulnerability of LOW severity is found',
'params':'none'
},
'VULNMEDIUM':
{
'description':'triggers if a vulnerability of MED sev... | 0.16398 | 0.145419 |
import math
import torch.nn as nn
import torch.nn.functional as F
from .multi_head_attention import *
class TransformerFeedForward(nn.Module):
def __init__(self, size, hidden_size, dropout_ratio=0.1, act="relu"):
super().__init__()
self.w_1 = nn.Linear(size, hidden_size)
self.w_2 = nn.Line... | misc/pytorch_toolkit/machine_translation/core/models/modules/transformer_encoder.py | import math
import torch.nn as nn
import torch.nn.functional as F
from .multi_head_attention import *
class TransformerFeedForward(nn.Module):
def __init__(self, size, hidden_size, dropout_ratio=0.1, act="relu"):
super().__init__()
self.w_1 = nn.Linear(size, hidden_size)
self.w_2 = nn.Line... | 0.948799 | 0.217732 |
from django import forms
from django.contrib.auth.forms import ReadOnlyPasswordHashField
from django.utils.translation import ugettext_lazy as _
from .models import User
class UserCreationForm(forms.ModelForm):
"""A form for creating new users. Includes all the required
fields, plus a repeated password."""
... | tovp/users/forms.py | from django import forms
from django.contrib.auth.forms import ReadOnlyPasswordHashField
from django.utils.translation import ugettext_lazy as _
from .models import User
class UserCreationForm(forms.ModelForm):
"""A form for creating new users. Includes all the required
fields, plus a repeated password."""
... | 0.640973 | 0.198608 |
import asyncio
import sys
from kopf import config
from kopf.k8s import events
# TODO: rename it it kopf.log()? kopf.events.log()? kopf.events.warn()?
async def event_async(obj, *, type, reason, message=''):
"""
Issue an event for the object.
"""
if isinstance(obj, (list, tuple)):
for item in ... | kopf/events.py | import asyncio
import sys
from kopf import config
from kopf.k8s import events
# TODO: rename it it kopf.log()? kopf.events.log()? kopf.events.warn()?
async def event_async(obj, *, type, reason, message=''):
"""
Issue an event for the object.
"""
if isinstance(obj, (list, tuple)):
for item in ... | 0.204183 | 0.050377 |
import argparse
import os, sys
import utils.data_util as data_util
import auto_patch
import time
import numpy as np
import gc
TOP_N = 1
parser = argparse.ArgumentParser()
parser.add_argument("-datadir", action = "store", default = "data", type = str)
parser.add_argument('-which_data', action = "store",
default = 'c... | arachne/main_rq6.py | import argparse
import os, sys
import utils.data_util as data_util
import auto_patch
import time
import numpy as np
import gc
TOP_N = 1
parser = argparse.ArgumentParser()
parser.add_argument("-datadir", action = "store", default = "data", type = str)
parser.add_argument('-which_data', action = "store",
default = 'c... | 0.111822 | 0.143998 |
import pandas as pd
import numpy as np
import pickle
import os
# Create directory if output directory does not already exist
cur_path = os.path.split(os.path.abspath(__file__))[0]
output_fldr = "csv_output_files"
output_dir = os.path.join(cur_path, output_fldr)
if not os.access(output_dir, os.F_OK):
os.makedirs(o... | ogusa/psid_summ_stats.py | import pandas as pd
import numpy as np
import pickle
import os
# Create directory if output directory does not already exist
cur_path = os.path.split(os.path.abspath(__file__))[0]
output_fldr = "csv_output_files"
output_dir = os.path.join(cur_path, output_fldr)
if not os.access(output_dir, os.F_OK):
os.makedirs(o... | 0.456652 | 0.195268 |
from bs4 import BeautifulSoup
import os,sys
import urllib.request, urllib.parse, http.cookiejar
import requests
import re
from datetime import datetime
def getcontent(url):
text_string = requests.get(url).text
#正则匹配出轮胎列表
html_string = re.findall(r'<a class=\\"item-name\\"(.*?)href=\\"//(.*?)target=\\"_bla... | spider/tuhu_tianmao.py | from bs4 import BeautifulSoup
import os,sys
import urllib.request, urllib.parse, http.cookiejar
import requests
import re
from datetime import datetime
def getcontent(url):
text_string = requests.get(url).text
#正则匹配出轮胎列表
html_string = re.findall(r'<a class=\\"item-name\\"(.*?)href=\\"//(.*?)target=\\"_bla... | 0.108756 | 0.102125 |
from typing import Dict, Optional, List, Mapping
import numpy as np
import datasets
from raft_baselines.classifiers.in_context_classifier import InContextClassifier
from raft_baselines.utils.gpt3_utils import (
complete,
search,
)
from raft_baselines.utils.tokenizers import TransformersTokenizer
GPT3_MAX_TOK... | src/raft_baselines/classifiers/gpt3_classifier.py | from typing import Dict, Optional, List, Mapping
import numpy as np
import datasets
from raft_baselines.classifiers.in_context_classifier import InContextClassifier
from raft_baselines.utils.gpt3_utils import (
complete,
search,
)
from raft_baselines.utils.tokenizers import TransformersTokenizer
GPT3_MAX_TOK... | 0.876813 | 0.335814 |
import datetime, json, os
from cantools import config
from cantools.util import log, write
from cantools.web import fetch, send_mail
from .actor import Actor
try:
import psutil
except ImportError as e:
pass # google crap engine (get it if you need it!)
from six import with_metaclass
class BotMeta(type):
def __new__... | cantools/scripts/pubsub/bots.py | import datetime, json, os
from cantools import config
from cantools.util import log, write
from cantools.web import fetch, send_mail
from .actor import Actor
try:
import psutil
except ImportError as e:
pass # google crap engine (get it if you need it!)
from six import with_metaclass
class BotMeta(type):
def __new__... | 0.107707 | 0.080033 |
import argparse
import json
import nnetwork
from nnetwork import load_checkpoint
import utility_functions
from utility_functions import imshow, process_image
import utility_functions
import torch
def predict(image_path, model, topk=5):
''' Predict the class (or classes) of an image using a trained deep learnin... | predict.py | import argparse
import json
import nnetwork
from nnetwork import load_checkpoint
import utility_functions
from utility_functions import imshow, process_image
import utility_functions
import torch
def predict(image_path, model, topk=5):
''' Predict the class (or classes) of an image using a trained deep learnin... | 0.620047 | 0.319692 |
from copy import deepcopy
from sqlalchemy import Column, Integer, ForeignKey
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.declarative import declared_attr, declarative_base
from models.common import BaseSchema
import models.common.overview as mco
import models.common.personnel as mcp
import mo... | marcotti/models/national.py | from copy import deepcopy
from sqlalchemy import Column, Integer, ForeignKey
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.declarative import declared_attr, declarative_base
from models.common import BaseSchema
import models.common.overview as mco
import models.common.personnel as mcp
import mo... | 0.609757 | 0.142202 |
import sys
sys.stdin = open("../input.txt", "r")
si = sys.stdin.readline
def check_row():
visited = [[0 for _ in range(n)] for _ in range(n)]
cnt = 0
for i in range(n):
check = True
last = board[i][0]
for j in range(n):
if board[i][j] == last:
continue
... | BOJ/simulation_boj/boj_14890.py | import sys
sys.stdin = open("../input.txt", "r")
si = sys.stdin.readline
def check_row():
visited = [[0 for _ in range(n)] for _ in range(n)]
cnt = 0
for i in range(n):
check = True
last = board[i][0]
for j in range(n):
if board[i][j] == last:
continue
... | 0.073682 | 0.112308 |
import pytest
from ais.ais_track import AISTrackList, ShipEta
from nmea.nmea_msg import AISMsgPayloadType1, AISMsgPayloadType5
def test_ais_track_list_single(dummy_ais_tracks_list_single):
track_list = AISTrackList(tracks=dummy_ais_tracks_list_single)
assert len(track_list.tracks) == 1
def test_ais_track_l... | tests/test_ais_track.py | import pytest
from ais.ais_track import AISTrackList, ShipEta
from nmea.nmea_msg import AISMsgPayloadType1, AISMsgPayloadType5
def test_ais_track_list_single(dummy_ais_tracks_list_single):
track_list = AISTrackList(tracks=dummy_ais_tracks_list_single)
assert len(track_list.tracks) == 1
def test_ais_track_l... | 0.520496 | 0.526708 |
from wxcloudrun.mapper.utils import get_table_column_name
from wxcloudrun.utils.SQL.DBUtils import db_utils
need_escapr_string_list = ["store_name"]
can_not_update_string_list = ["openid", "store_id"]
def insert_store_info(store_id, store_name, dsr, openid, ignore=False):
sql_str = '''
insert {ignore} into s... | wxcloudrun/mapper/store_info.py | from wxcloudrun.mapper.utils import get_table_column_name
from wxcloudrun.utils.SQL.DBUtils import db_utils
need_escapr_string_list = ["store_name"]
can_not_update_string_list = ["openid", "store_id"]
def insert_store_info(store_id, store_name, dsr, openid, ignore=False):
sql_str = '''
insert {ignore} into s... | 0.215186 | 0.104981 |
import sys,time, csv,numpy as np,argparse,string,os,math,logging
import numpy as np
import iolib
import lmem_func
import multiprocessing as mp
parser = argparse.ArgumentParser(description='Linear mixed effect models for quantitative traits')
parser.add_argument('-genfile', metavar='genfile',default="", type=str, help=... | lmem.py | import sys,time, csv,numpy as np,argparse,string,os,math,logging
import numpy as np
import iolib
import lmem_func
import multiprocessing as mp
parser = argparse.ArgumentParser(description='Linear mixed effect models for quantitative traits')
parser.add_argument('-genfile', metavar='genfile',default="", type=str, help=... | 0.169991 | 0.096153 |
import numpy as np
from math import floor
import vapoursynth as vs
from .utils import frame_to_array
from .masking import eoe_convolution
from .errors import VariableFormatError, YUVError
from typing import Callable, Dict, Any
core = vs.core
def chromashifter(
clip: vs.VideoNode, wthresh: int = 31, vertical: bo... | rvsfunc/dvd.py | import numpy as np
from math import floor
import vapoursynth as vs
from .utils import frame_to_array
from .masking import eoe_convolution
from .errors import VariableFormatError, YUVError
from typing import Callable, Dict, Any
core = vs.core
def chromashifter(
clip: vs.VideoNode, wthresh: int = 31, vertical: bo... | 0.870694 | 0.601535 |
import sys
import time
from unittest import mock
from unittest.mock import patch
import matplotlib.pyplot as plt
import pytest
from pyinfer import InferenceReport, MultiInferenceReport
from pyinfer.errors import (
MatplotlibNotInstalledError,
MeasurementIntervalNotSetError,
ModelIsNotCallableError,
Nam... | test/test_multi_inference_report.py | import sys
import time
from unittest import mock
from unittest.mock import patch
import matplotlib.pyplot as plt
import pytest
from pyinfer import InferenceReport, MultiInferenceReport
from pyinfer.errors import (
MatplotlibNotInstalledError,
MeasurementIntervalNotSetError,
ModelIsNotCallableError,
Nam... | 0.54577 | 0.457258 |
# Time complexities:
# Best case -> O(n) -> When v is equal to to_find
# Average case -> O(nlog(n))
# Worst case -> O(nlog(n))
from random import randint
numbs = [int(i) for i in input().split(' ')]
to_find = int(input())
def selection(numbs: list, to_find: int) -> int:
"""
Generate a random number between ... | misc/selection.py |
# Time complexities:
# Best case -> O(n) -> When v is equal to to_find
# Average case -> O(nlog(n))
# Worst case -> O(nlog(n))
from random import randint
numbs = [int(i) for i in input().split(' ')]
to_find = int(input())
def selection(numbs: list, to_find: int) -> int:
"""
Generate a random number between ... | 0.708717 | 0.705145 |
from django.test import TestCase
from django.urls import reverse
from .. import models
class AdminAddBusinessTestCase(TestCase):
""" Tests of who can add businesses """
fixtures = [
"sectors",
"business_types",
"business_statuses",
"regions",
"test_admin_add_business"... | biz_portal/apps/portal/tests/test_admin.py | from django.test import TestCase
from django.urls import reverse
from .. import models
class AdminAddBusinessTestCase(TestCase):
""" Tests of who can add businesses """
fixtures = [
"sectors",
"business_types",
"business_statuses",
"regions",
"test_admin_add_business"... | 0.492188 | 0.410047 |
import sys
from PyQt5.QtWidgets import QPushButton,QMessageBox,QVBoxLayout,QWidget,QApplication,QMainWindow
class QMessageBoxDemo(QMainWindow):
def __init__(self):
super(QMessageBoxDemo, self).__init__()
#设置窗口大小
self.resize(400, 150)
#设置窗口标题
self.setWindowTitle("QMessageBox... | 06-QMessageBox/QMessageBox1.py | import sys
from PyQt5.QtWidgets import QPushButton,QMessageBox,QVBoxLayout,QWidget,QApplication,QMainWindow
class QMessageBoxDemo(QMainWindow):
def __init__(self):
super(QMessageBoxDemo, self).__init__()
#设置窗口大小
self.resize(400, 150)
#设置窗口标题
self.setWindowTitle("QMessageBox... | 0.127462 | 0.089654 |
import wpilib
from magicbot import MagicRobot
from rev import CANSparkMax, MotorType
from wpilib.buttons import JoystickButton
from ctre import WPI_VictorSPX
class Robot(MagicRobot):
def createObjects(self):
self.joystick = wpilib.Joystick(2)
self.winch_button = JoystickButton(self.joystick, 6)
... | electrical_test/robot.py | import wpilib
from magicbot import MagicRobot
from rev import CANSparkMax, MotorType
from wpilib.buttons import JoystickButton
from ctre import WPI_VictorSPX
class Robot(MagicRobot):
def createObjects(self):
self.joystick = wpilib.Joystick(2)
self.winch_button = JoystickButton(self.joystick, 6)
... | 0.267026 | 0.167253 |
import gc
import logging
logging.getLogger('transformers').setLevel(logging.WARNING)
from random import choice
import torch
import torch.nn.functional as F
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Total length of the paragraph to generate.
PARAGRAPH_LENGTH = 100
# Max number of words to process at on... | genesis/AIText.py | import gc
import logging
logging.getLogger('transformers').setLevel(logging.WARNING)
from random import choice
import torch
import torch.nn.functional as F
from transformers import GPT2Tokenizer, GPT2LMHeadModel
# Total length of the paragraph to generate.
PARAGRAPH_LENGTH = 100
# Max number of words to process at on... | 0.747892 | 0.385751 |
import re
import logging
import schema
import subprocess
from hil.model import db, Switch, Port, BigIntegerType, SwitchSession
from hil.errors import SwitchError
from hil.ext.switches.common import string_to_dict, string_to_list
logger = logging.getLogger(__name__)
# Class layout
# 1. Public methods:
# 2. Private me... | hil/ext/switches/ovs.py | import re
import logging
import schema
import subprocess
from hil.model import db, Switch, Port, BigIntegerType, SwitchSession
from hil.errors import SwitchError
from hil.ext.switches.common import string_to_dict, string_to_list
logger = logging.getLogger(__name__)
# Class layout
# 1. Public methods:
# 2. Private me... | 0.673514 | 0.170681 |
import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
from textclf.config.components import RNNConfig
class RNN(torch.nn.Module):
"""
rnn layer
"""
supported_types = ["RNN", "GRU", "LSTM"]
def __init__(self, co... | textclf/models/classifier/components.py | import torch
import torch.nn as nn
from torch.nn.utils.rnn import pack_padded_sequence, pad_packed_sequence
import torch.nn.functional as F
from textclf.config.components import RNNConfig
class RNN(torch.nn.Module):
"""
rnn layer
"""
supported_types = ["RNN", "GRU", "LSTM"]
def __init__(self, co... | 0.926562 | 0.281955 |
"""Make a histogram and calculate entropy of files."""
import math
import os.path
import subprocess as sp
import sys
def main(argv):
"""
Entry point for histdata.
Arguments:
argv: List of file names.
"""
if len(argv) < 1:
sys.exit(1)
for fn in argv:
hdata, size = read... | histdata.py | """Make a histogram and calculate entropy of files."""
import math
import os.path
import subprocess as sp
import sys
def main(argv):
"""
Entry point for histdata.
Arguments:
argv: List of file names.
"""
if len(argv) < 1:
sys.exit(1)
for fn in argv:
hdata, size = read... | 0.652906 | 0.580738 |
import os
import fabric
from inspect import getsource
from textwrap import dedent
from fabric.api import run, sudo, cd
fabric.state.env.colorize_errors = True
fabric.state.output['stdout'] = False
# task const variables
VARS = dict(
current_user=fabric.state.env.user,
# project settings
project_name='ai... | provision/fabric_provisioner.py | import os
import fabric
from inspect import getsource
from textwrap import dedent
from fabric.api import run, sudo, cd
fabric.state.env.colorize_errors = True
fabric.state.output['stdout'] = False
# task const variables
VARS = dict(
current_user=fabric.state.env.user,
# project settings
project_name='ai... | 0.190498 | 0.065098 |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('movies', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
... | backend/reviews/migrations/0001_initial.py |
from django.conf import settings
from django.db import migrations, models
import django.db.models.deletion
class Migration(migrations.Migration):
initial = True
dependencies = [
('movies', '0001_initial'),
migrations.swappable_dependency(settings.AUTH_USER_MODEL),
]
operations = [
... | 0.521715 | 0.176814 |
import numpy
import re
SAVE_PATH = '/home/bioprober/gh/3d-converter/tests/data/'
class TransWriter:
def __init__(self, filename):
self.filename = filename
self.info = ''
def config(self, axis, degree, steps=65, move=(0, 0, 0)):
"""
Configs self.info string by given paramete... | misc/trans_writer.py | import numpy
import re
SAVE_PATH = '/home/bioprober/gh/3d-converter/tests/data/'
class TransWriter:
def __init__(self, filename):
self.filename = filename
self.info = ''
def config(self, axis, degree, steps=65, move=(0, 0, 0)):
"""
Configs self.info string by given paramete... | 0.710327 | 0.6137 |
import os
from keystoneauth1.identity import v3
from keystoneauth1 import session
from swiftclient import client as swift_client
class SwiftHandler:
"""
Initiates a connection to a project domain.
"""
@staticmethod
def get_scoped_session(os_auth_url, username, password, os_project_domain, os_pro... | src/swift/__init__.py | import os
from keystoneauth1.identity import v3
from keystoneauth1 import session
from swiftclient import client as swift_client
class SwiftHandler:
"""
Initiates a connection to a project domain.
"""
@staticmethod
def get_scoped_session(os_auth_url, username, password, os_project_domain, os_pro... | 0.585931 | 0.097519 |
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import grad
__all__ = ['BCEGANLoss', 'LSGANLoss', 'WGANGPLoss']
class BCEGANLoss(nn.Module):
def __init__(self):
super(BCEGANLoss, self).__init__()
self.bce_loss = nn.BCELoss()
self.register_buffer('tar... | losses/gan_loss.py | import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.autograd import grad
__all__ = ['BCEGANLoss', 'LSGANLoss', 'WGANGPLoss']
class BCEGANLoss(nn.Module):
def __init__(self):
super(BCEGANLoss, self).__init__()
self.bce_loss = nn.BCELoss()
self.register_buffer('tar... | 0.936234 | 0.338282 |
import re
import subprocess
import time
from .exceptions import InterfaceNotFoundError
from control.utils.iface_statistics.stats_reader import StatsReader
class InterfaceStatisticsIp(StatsReader):
@staticmethod
def _call_ip() -> str:
cmd = ['ip', '-s', 'link']
completed_process = subprocess.r... | clients/control/utils/iface_statistics/ip.py | import re
import subprocess
import time
from .exceptions import InterfaceNotFoundError
from control.utils.iface_statistics.stats_reader import StatsReader
class InterfaceStatisticsIp(StatsReader):
@staticmethod
def _call_ip() -> str:
cmd = ['ip', '-s', 'link']
completed_process = subprocess.r... | 0.52975 | 0.118538 |
import unittest
from password_locker import User, Credential
class TestUser(unittest.TestCase):
'''
Test class that defines test cases for the user class behaviors.
Args:
unittest.TestCase: helps in creating test cases
'''
def setUp(self):
'''
Function to create a user ac... | test_password.py | import unittest
from password_locker import User, Credential
class TestUser(unittest.TestCase):
'''
Test class that defines test cases for the user class behaviors.
Args:
unittest.TestCase: helps in creating test cases
'''
def setUp(self):
'''
Function to create a user ac... | 0.608594 | 0.488832 |
import datetime as dt
import json
import os
from urllib import error, request
import pandas as pd
import pyodbc as sql
# TODO: Add argument parsing to allow for command-line options, thinking primarily of refreshing a specific user vs. entire dataset
# Could do more like only those active within last month, only one ... | scheduled/UpdateUsernameXRef.py | import datetime as dt
import json
import os
from urllib import error, request
import pandas as pd
import pyodbc as sql
# TODO: Add argument parsing to allow for command-line options, thinking primarily of refreshing a specific user vs. entire dataset
# Could do more like only those active within last month, only one ... | 0.084768 | 0.076442 |
import argparse
import numpy as np
import chainer.functions as F
from consts import activation_func,dtypes,uplayer,downlayer,norm_layer,unettype,optim
import os,sys
from datetime import datetime as dt
def arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-g', type=int, default=0,
... | arguments.py | import argparse
import numpy as np
import chainer.functions as F
from consts import activation_func,dtypes,uplayer,downlayer,norm_layer,unettype,optim
import os,sys
from datetime import datetime as dt
def arguments():
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', '-g', type=int, default=0,
... | 0.545528 | 0.089813 |
from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from rest_framework.permissions import BasePermission, AllowAny, IsAdminUser, IsAuthenticated
from . import models, serializers
from gemtown.users import models as user_models
from gemtown.modelers im... | gemtown/modelphotos/views.py | from rest_framework.views import APIView
from rest_framework.response import Response
from rest_framework import status
from rest_framework.permissions import BasePermission, AllowAny, IsAdminUser, IsAuthenticated
from . import models, serializers
from gemtown.users import models as user_models
from gemtown.modelers im... | 0.377655 | 0.083853 |
import os
import json
import math
import matplotlib as maplot
import matplotlib.pyplot as pyplot
from datetime import datetime
# User library
from sub.inputprocess import CONSTANTS as CONS
# **json.loads(json_data)
def get_data():
"""Read output file to get data."""
try:
with open(CONS["OUTPUT_FILE"]... | src/sub/plot.py | import os
import json
import math
import matplotlib as maplot
import matplotlib.pyplot as pyplot
from datetime import datetime
# User library
from sub.inputprocess import CONSTANTS as CONS
# **json.loads(json_data)
def get_data():
"""Read output file to get data."""
try:
with open(CONS["OUTPUT_FILE"]... | 0.64232 | 0.49939 |
import os
import numpy as np
import ray
import torch
from gym_ds3.schedulers.scarl.data_worker_scarl import SCARLWorker
from gym_ds3.schedulers.scarl.parameter_server import ParameterServer
from config import args
args.scheduler_name = 'scarl'
if args.use_tensorboard:
from tensorboardX import SummaryWriter
... | run_scarl_scheduler.py | import os
import numpy as np
import ray
import torch
from gym_ds3.schedulers.scarl.data_worker_scarl import SCARLWorker
from gym_ds3.schedulers.scarl.parameter_server import ParameterServer
from config import args
args.scheduler_name = 'scarl'
if args.use_tensorboard:
from tensorboardX import SummaryWriter
... | 0.441432 | 0.191063 |
import graphene
import json
import os
import sys
import hashlib
pf = os.path.join(sys.path[0],'..', 'planets.json')
with open(pf) as f:
planets = json.load(f)
pf = os.path.join(sys.path[0],'..', 'satellites.json')
with open(pf) as f:
satellites = json.load(f)
class Planet(graphene.ObjectType):
... | 2020/GraphQL/generate-flag/index.py | import graphene
import json
import os
import sys
import hashlib
pf = os.path.join(sys.path[0],'..', 'planets.json')
with open(pf) as f:
planets = json.load(f)
pf = os.path.join(sys.path[0],'..', 'satellites.json')
with open(pf) as f:
satellites = json.load(f)
class Planet(graphene.ObjectType):
... | 0.356111 | 0.244386 |
from src.view_app.View import *
class MainView(View):
"""Vista Principal del programa."""
def _init_view(self):
"""Método de creación de vista.
En este método es donde realmente se genera la vista de la,
ventana pues es donde se crean todos los widgets y se colocán,
en su lug... | 04Cuarto/Sistemas_y_proteccion_de_sistemas_informaticos_SPSI/Practica5/src/view_app/Views.py | from src.view_app.View import *
class MainView(View):
"""Vista Principal del programa."""
def _init_view(self):
"""Método de creación de vista.
En este método es donde realmente se genera la vista de la,
ventana pues es donde se crean todos los widgets y se colocán,
en su lug... | 0.449272 | 0.330471 |