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 |
|---|---|---|---|---|
from typing import List, Any, Union
import torch
from dlex.torch.utils.ops_utils import maybe_cuda
from torch import LongTensor
def pad_sequence(
data: List[List[Any]],
padding_value,
max_len: int = None,
output_tensor: bool = False,
dim: int = 2):
if dim == 3... | dlex/torch/utils/variable_length_tensor.py | from typing import List, Any, Union
import torch
from dlex.torch.utils.ops_utils import maybe_cuda
from torch import LongTensor
def pad_sequence(
data: List[List[Any]],
padding_value,
max_len: int = None,
output_tensor: bool = False,
dim: int = 2):
if dim == 3... | 0.67822 | 0.421611 |
import numpy as np
from sklearn.metrics import confusion_matrix
def compute_score(y_test, y_pred, verbose=True):
conf = confusion_matrix(y_test, y_pred, labels=[0,1])
TN, FP, FN, TP = conf.ravel()
TPR = np.nan_to_num(round(TP / (TP + FN), 4))
SPC = np.nan_to_num(round(TN / (FP + TN), 4))
PPV = np.... | python/irc_utils.py | import numpy as np
from sklearn.metrics import confusion_matrix
def compute_score(y_test, y_pred, verbose=True):
conf = confusion_matrix(y_test, y_pred, labels=[0,1])
TN, FP, FN, TP = conf.ravel()
TPR = np.nan_to_num(round(TP / (TP + FN), 4))
SPC = np.nan_to_num(round(TN / (FP + TN), 4))
PPV = np.... | 0.627267 | 0.547162 |
from __future__ import unicode_literals
import pendulum
locale = 'de'
def test_diff_for_humans():
with pendulum.test(pendulum.datetime(2016, 8, 29)):
diff_for_humans()
def diff_for_humans():
d = pendulum.now().subtract(seconds=1)
assert d.diff_for_humans(locale=locale) == 'vor 1 Sekunde'
... | tests/localization/test_de.py | from __future__ import unicode_literals
import pendulum
locale = 'de'
def test_diff_for_humans():
with pendulum.test(pendulum.datetime(2016, 8, 29)):
diff_for_humans()
def diff_for_humans():
d = pendulum.now().subtract(seconds=1)
assert d.diff_for_humans(locale=locale) == 'vor 1 Sekunde'
... | 0.664867 | 0.698098 |
from maya import cmds
from maya import mel
import os, json
import datetime as dt
def formatPath(path):
path = path.replace("/", os.sep)
path = path.replace("\\", os.sep)
return path
mayaAppDir = formatPath(mel.eval('getenv MAYA_APP_DIR'))
scriptsDir = formatPath(mayaAppDir + os.sep + 'scripts')
projectD... | service/setup.py | from maya import cmds
from maya import mel
import os, json
import datetime as dt
def formatPath(path):
path = path.replace("/", os.sep)
path = path.replace("\\", os.sep)
return path
mayaAppDir = formatPath(mel.eval('getenv MAYA_APP_DIR'))
scriptsDir = formatPath(mayaAppDir + os.sep + 'scripts')
projectD... | 0.116337 | 0.04888 |
from django.test import TestCase
from cyder.cydhcp.vlan.models import Vlan
from cyder.cydhcp.site.models import Site
from cyder.cydhcp.range.models import Range
from cyder.cydhcp.network.models import Network
from cyder.cydhcp.interface.static_intr.models import StaticInterface
from cyder.cydhcp.lib.utils import calc_... | cyder/cydhcp/lib/tests/free_ip.py | from django.test import TestCase
from cyder.cydhcp.vlan.models import Vlan
from cyder.cydhcp.site.models import Site
from cyder.cydhcp.range.models import Range
from cyder.cydhcp.network.models import Network
from cyder.cydhcp.interface.static_intr.models import StaticInterface
from cyder.cydhcp.lib.utils import calc_... | 0.613468 | 0.272853 |
import os
import unittest
import pfnet as pf
import numpy as np
from . import test_cases
from numpy.linalg import norm
class TestBranches(unittest.TestCase):
def setUp(self):
# Random
np.random.seed(0)
def test_power_flow_utils(self):
h = 1e-8
tol = 1e-5
eps = 1.0 #... | tests/test_branches.py |
import os
import unittest
import pfnet as pf
import numpy as np
from . import test_cases
from numpy.linalg import norm
class TestBranches(unittest.TestCase):
def setUp(self):
# Random
np.random.seed(0)
def test_power_flow_utils(self):
h = 1e-8
tol = 1e-5
eps = 1.0 #... | 0.393968 | 0.358493 |
import argparse
import torch
import numpy as np
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "3,6"
parser = argparse.ArgumentParser(description='PyTorch implementation of pre-training of graph neural networks')
parser.add_argument('--device', type=int, default=0,
help='which gpu to use if any ... | main_training.py | import argparse
import torch
import numpy as np
import os
# os.environ["CUDA_VISIBLE_DEVICES"] = "3,6"
parser = argparse.ArgumentParser(description='PyTorch implementation of pre-training of graph neural networks')
parser.add_argument('--device', type=int, default=0,
help='which gpu to use if any ... | 0.444565 | 0.0745 |
from matplotlib import cm
from cicliminds_lib.utils import patch_config
from cicliminds_lib.bindings import FLOAT_MISSING_VALUE
from cicliminds_lib.bindings import nco_fldmean_from_data
from cicliminds_lib.math import xr_weighted_median
from cicliminds_lib.math import xr_weighted_quantile
from cicliminds_lib.unify.mod... | cicliminds_lib/plotting/recipes/time_series.py | from matplotlib import cm
from cicliminds_lib.utils import patch_config
from cicliminds_lib.bindings import FLOAT_MISSING_VALUE
from cicliminds_lib.bindings import nco_fldmean_from_data
from cicliminds_lib.math import xr_weighted_median
from cicliminds_lib.math import xr_weighted_quantile
from cicliminds_lib.unify.mod... | 0.709523 | 0.37419 |
from campy.graphics.gobjects import GOval, GRect, GLabel, GLine, GPolygon, GArc
from campy.graphics.gwindow import GWindow
def main():
"""
TODO:
"""
window=GWindow(500,400,title='cute') #頭
head=GOval(200,180)
head.filled =True
head.fill_color='khaki'
head.color='khaki'
window.add(h... | StanCode_project/my_drawing/my_drawing.py | from campy.graphics.gobjects import GOval, GRect, GLabel, GLine, GPolygon, GArc
from campy.graphics.gwindow import GWindow
def main():
"""
TODO:
"""
window=GWindow(500,400,title='cute') #頭
head=GOval(200,180)
head.filled =True
head.fill_color='khaki'
head.color='khaki'
window.add(h... | 0.188548 | 0.182389 |
__author__ = '<NAME>'
import models.mainmodel as model
import models.dataio as dataio
import models.abstractplugin as abstractplugin
import models.config as config
import models.ultrasonicgate as ultrasonicgate
import controllers.pathfinder as pathfinder
from utils.skiptest import skipIfModuleNotInstalled
import h5py
... | models/tests/test_mainmodel.py | __author__ = '<NAME>'
import models.mainmodel as model
import models.dataio as dataio
import models.abstractplugin as abstractplugin
import models.config as config
import models.ultrasonicgate as ultrasonicgate
import controllers.pathfinder as pathfinder
from utils.skiptest import skipIfModuleNotInstalled
import h5py
... | 0.533884 | 0.220867 |
from __future__ import annotations
__all__ = (
"extract_codecs",
"extract_codec_metadata",
"extract_default_codec",
"insert_default_codec",
"EncodeFunction",
"DecodeFunction",
"Codec",
)
from typing import (
cast,
overload,
final,
Any,
Type,
TypeVar,
Generic,
... | src/testplates/impl/base/codec.py | from __future__ import annotations
__all__ = (
"extract_codecs",
"extract_codec_metadata",
"extract_default_codec",
"insert_default_codec",
"EncodeFunction",
"DecodeFunction",
"Codec",
)
from typing import (
cast,
overload,
final,
Any,
Type,
TypeVar,
Generic,
... | 0.870996 | 0.274753 |
import re
__authors__ = """
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
"""
ABBREVS = {
('Bl', '.', 'a', '.'): 'Bl.a.',
('Bl', 'a'): 'Bl.a',
('Bl.a', '.'): 'Bl.a.',
('D', 'v', 's'): 'D.v.s',
('Fr', 'o', 'm'): 'Fr.o.m',
('O', 's', 'v'): 'O.s.v',
('S', 'k'): 'S.k',
('T', '.', 'ex', '.'): 'T.ex.',
... | tokenizer.py | import re
__authors__ = """
<NAME> <<EMAIL>>
<NAME> <<EMAIL>>
"""
ABBREVS = {
('Bl', '.', 'a', '.'): 'Bl.a.',
('Bl', 'a'): 'Bl.a',
('Bl.a', '.'): 'Bl.a.',
('D', 'v', 's'): 'D.v.s',
('Fr', 'o', 'm'): 'Fr.o.m',
('O', 's', 'v'): 'O.s.v',
('S', 'k'): 'S.k',
('T', '.', 'ex', '.'): 'T.ex.',
... | 0.511229 | 0.199055 |
from django.http import JsonResponse
from django.views import View
incoming_requests = {
"incoming_requests": [
{
"requestor": "<NAME>",
"request": {
"type": "HR044 - Leave Application",
"id": "HR-015715"
},
"history": [
{
"date": "Monday 26 June 2017",
... | demo/src/wss_ui/site/fake_data_views/incoming_requests.py | from django.http import JsonResponse
from django.views import View
incoming_requests = {
"incoming_requests": [
{
"requestor": "<NAME>",
"request": {
"type": "HR044 - Leave Application",
"id": "HR-015715"
},
"history": [
{
"date": "Monday 26 June 2017",
... | 0.426083 | 0.276443 |
import numpy as np
from wepy.util.util import box_vectors_to_lengths_angles
from geomm.recentering import recenter_pair
from geomm.superimpose import superimpose
from geomm.rmsd import calc_rmsd
from wepy.resampling.distances.distance import Distance
class UnbindingDistance(Distance):
def __init__(self, ligand_... | wepy/resampling/distances/receptor.py | import numpy as np
from wepy.util.util import box_vectors_to_lengths_angles
from geomm.recentering import recenter_pair
from geomm.superimpose import superimpose
from geomm.rmsd import calc_rmsd
from wepy.resampling.distances.distance import Distance
class UnbindingDistance(Distance):
def __init__(self, ligand_... | 0.666605 | 0.444324 |
# Copyright 2020-2022 Blue Brain Project / EPFL
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agree... | emodelrunner/output.py |
# Copyright 2020-2022 Blue Brain Project / EPFL
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agree... | 0.869659 | 0.359252 |
from ...utils.yaml import yaml
from ...utils.collections import OrderedDict
from .reader import Reader
from .locator import Locator
from .exceptions import ReaderSyntaxError
from .locator import (LocatableString, LocatableInt, LocatableFloat)
# YAML mapping tag
MAP_TAG = u'tag:yaml.org,2002:map'
# This is an intern... | aria/parser/reading/yaml.py |
from ...utils.yaml import yaml
from ...utils.collections import OrderedDict
from .reader import Reader
from .locator import Locator
from .exceptions import ReaderSyntaxError
from .locator import (LocatableString, LocatableInt, LocatableFloat)
# YAML mapping tag
MAP_TAG = u'tag:yaml.org,2002:map'
# This is an intern... | 0.533154 | 0.1692 |
import torch
import torch.nn as nn
import hdvw.models.alexnet_dnn_block as alexnet_dnn
import hdvw.models.alexnet_mcdo_block as alexnet_mcdo
import hdvw.models.smoothing_block as smoothing
import hdvw.models.classifier_block as classifier
class AlexNet(nn.Module):
def __init__(self, block,
sbloc... | hdvw/models/alexnet.py | import torch
import torch.nn as nn
import hdvw.models.alexnet_dnn_block as alexnet_dnn
import hdvw.models.alexnet_mcdo_block as alexnet_mcdo
import hdvw.models.smoothing_block as smoothing
import hdvw.models.classifier_block as classifier
class AlexNet(nn.Module):
def __init__(self, block,
sbloc... | 0.896635 | 0.313026 |
import itertools
import json
import os
import sys
names = [
"language",
"subset_uri",
"fail_on_first_error",
"provide_detailed_stack_trace",
"check_uri_compliance",
"check_completeness",
"check_best_practices",
"uri_prefix",
"version",
"insert_type",
"test_equality",
"return_file",
"main_file_name",
"diff_file_name",... | src/test/generate_test_cases.py | import itertools
import json
import os
import sys
names = [
"language",
"subset_uri",
"fail_on_first_error",
"provide_detailed_stack_trace",
"check_uri_compliance",
"check_completeness",
"check_best_practices",
"uri_prefix",
"version",
"insert_type",
"test_equality",
"return_file",
"main_file_name",
"diff_file_name",... | 0.164014 | 0.200734 |
# Copyright 2019 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""
Install Debian sysroots for cross compiling Open Screen.
"""
# The sysroot is needed to ensure that binaries that get built will run on
# the oldest st... | build/scripts/install-sysroot.py |
# Copyright 2019 The Chromium Authors. All rights reserved.
# Use of this source code is governed by a BSD-style license that can be
# found in the LICENSE file.
"""
Install Debian sysroots for cross compiling Open Screen.
"""
# The sysroot is needed to ensure that binaries that get built will run on
# the oldest st... | 0.596668 | 0.282689 |
from typing import Tuple, Union
import torch
from torchvision.transforms import ToTensor as ImageToTensor
from data.transforms import (
MaskToTensor,
DynamicSquareCrop,
Resize,
stabilized_padding,
)
from data.Human36M.skeleton import JOINTS
from data.Human36M.statistics import MEDIAN_PIXEL
from geome... | data/Human36M/transform.py | from typing import Tuple, Union
import torch
from torchvision.transforms import ToTensor as ImageToTensor
from data.transforms import (
MaskToTensor,
DynamicSquareCrop,
Resize,
stabilized_padding,
)
from data.Human36M.skeleton import JOINTS
from data.Human36M.statistics import MEDIAN_PIXEL
from geome... | 0.863334 | 0.581362 |
from time import sleep
import os
import platform
import pyASCIIgenerator
# # # # # # # # # # # # #
# INFO #
# # # # # # # # # # # # #
logofile = open('logo.txt', 'r')
logolines = logofile.readlines()
for line in logolines:
print("{}".format(line.strip()))
print('pyCMD by HYKANTUS')... | main.py | from time import sleep
import os
import platform
import pyASCIIgenerator
# # # # # # # # # # # # #
# INFO #
# # # # # # # # # # # # #
logofile = open('logo.txt', 'r')
logolines = logofile.readlines()
for line in logolines:
print("{}".format(line.strip()))
print('pyCMD by HYKANTUS')... | 0.051678 | 0.042404 |
from perf_tools import histogram_metric
from telemetry.page import page_measurement
MEMORY_HISTOGRAMS = [
{'name': 'V8.MemoryExternalFragmentationTotal', 'units': 'percent'},
{'name': 'V8.MemoryHeapSampleTotalCommitted', 'units': 'kb'},
{'name': 'V8.MemoryHeapSampleTotalUsed', 'units': 'kb'},
{'name': ... | tools/perf/perf_tools/memory_measurement.py | from perf_tools import histogram_metric
from telemetry.page import page_measurement
MEMORY_HISTOGRAMS = [
{'name': 'V8.MemoryExternalFragmentationTotal', 'units': 'percent'},
{'name': 'V8.MemoryHeapSampleTotalCommitted', 'units': 'kb'},
{'name': 'V8.MemoryHeapSampleTotalUsed', 'units': 'kb'},
{'name': ... | 0.672977 | 0.181118 |
import unittest
import UserDict
from cStringIO import StringIO
from zope import interface
from zope import component
from zope import schema
from zope.copypastemove.interfaces import IObjectCopier, IObjectMover
from zope.location.traversing import LocationPhysicallyLocatable
from zope.traversing.adapters import Traver... | src/z3c/dav/tests/test_copymove.py | import unittest
import UserDict
from cStringIO import StringIO
from zope import interface
from zope import component
from zope import schema
from zope.copypastemove.interfaces import IObjectCopier, IObjectMover
from zope.location.traversing import LocationPhysicallyLocatable
from zope.traversing.adapters import Traver... | 0.505859 | 0.203925 |
import numpy as np
import metriks
def test_label_mrr_with_errors():
y_true = np.array([[0, 1, 1, 0],
[1, 1, 1, 0],
[0, 1, 1, 1]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4],
[0.9, 0.2, 0.8, 0.4],
[0.2, 0.8, 0.9, 0.4]])
... | tests/test_label_mean_reciprocal_rank.py | import numpy as np
import metriks
def test_label_mrr_with_errors():
y_true = np.array([[0, 1, 1, 0],
[1, 1, 1, 0],
[0, 1, 1, 1]])
y_prob = np.array([[0.2, 0.9, 0.8, 0.4],
[0.9, 0.2, 0.8, 0.4],
[0.2, 0.8, 0.9, 0.4]])
... | 0.796767 | 0.700495 |
from bpy.types import Context, Operator
from bpy.props import BoolProperty
from typing import Set
from .body_group import BodyGroup
class MaleBody(Operator):
"""Add imvu mesh primitive male body parts to scene"""
group = BodyGroup("male", [("head", "eyes", "brows", "lashes"), "torso", "hands", "legs", "calfs"... | src/ops/m_body.py | from bpy.types import Context, Operator
from bpy.props import BoolProperty
from typing import Set
from .body_group import BodyGroup
class MaleBody(Operator):
"""Add imvu mesh primitive male body parts to scene"""
group = BodyGroup("male", [("head", "eyes", "brows", "lashes"), "torso", "hands", "legs", "calfs"... | 0.902516 | 0.463687 |
import requests
from plugins.oob import verify_request, gen_oob_domain
# Vuln Base Info
def info():
return {
"author": "cckuailong",
"name": '''Seagate BlackArmor NAS - Command Injection''',
"description": '''Seagate BlackArmor NAS allows remote attackers to execute arbitrary code via the... | 2014/CVE-2014-3206/poc/pocsploit/CVE-2014-3206.py | import requests
from plugins.oob import verify_request, gen_oob_domain
# Vuln Base Info
def info():
return {
"author": "cckuailong",
"name": '''Seagate BlackArmor NAS - Command Injection''',
"description": '''Seagate BlackArmor NAS allows remote attackers to execute arbitrary code via the... | 0.213131 | 0.164987 |
import _kaldilm
def arpa2fst(input_arpa: str,
output_fst: str = '',
bos_symbol: str = '<s>',
disambig_symbol: str = '',
eos_symbol: str = '</s>',
ilabel_sort: bool = True,
keep_symbols: bool = False,
max_arpa_warnings: int = 3... | kaldilm/python/kaldilm/arpa2fst.py |
import _kaldilm
def arpa2fst(input_arpa: str,
output_fst: str = '',
bos_symbol: str = '<s>',
disambig_symbol: str = '',
eos_symbol: str = '</s>',
ilabel_sort: bool = True,
keep_symbols: bool = False,
max_arpa_warnings: int = 3... | 0.774583 | 0.286344 |
import re
RUBY_SPEC_MATCHER = re.compile(r"_spec\.rb$")
RUBY_SOUCE_MATCHER = re.compile(r"\.rb$")
RUBY_SPECIAL_SPEC_MATCHER = re.compile(r"(\.erb|\.haml|\.slim|\.jbuilder)_spec\.rb$")
RUBY_SPECIAL_SOURCE_MATCHER = re.compile(r"\.erb$|\.haml$|\.slim$|\.jbuilder$")
JS_SPEC_MATCHER = re.compile(r"-test\.js$")
PYTHON_SPEC... | resolver.py | import re
RUBY_SPEC_MATCHER = re.compile(r"_spec\.rb$")
RUBY_SOUCE_MATCHER = re.compile(r"\.rb$")
RUBY_SPECIAL_SPEC_MATCHER = re.compile(r"(\.erb|\.haml|\.slim|\.jbuilder)_spec\.rb$")
RUBY_SPECIAL_SOURCE_MATCHER = re.compile(r"\.erb$|\.haml$|\.slim$|\.jbuilder$")
JS_SPEC_MATCHER = re.compile(r"-test\.js$")
PYTHON_SPEC... | 0.277669 | 0.115038 |
import os
import errno
import sys
import traceback
from time import sleep
import unicodedata
from config import patches_dir
import git_tracker as git
import svn_tracker as svn
import tracker
def start_git_revision_tracker():
print("Starting revision tracker...")
iteration_count = 0
co... | app.py | import os
import errno
import sys
import traceback
from time import sleep
import unicodedata
from config import patches_dir
import git_tracker as git
import svn_tracker as svn
import tracker
def start_git_revision_tracker():
print("Starting revision tracker...")
iteration_count = 0
co... | 0.056953 | 0.060308 |
from __future__ import absolute_import
import six
import numpy as np
import chainer
from chainer.backends import cuda
from chainer import function
from chainer.utils import type_check
from ..loss._helper import to_onehot
class DiscreteJaccard(function.Function):
""" Jaccard index
NOTE: This is not a differen... | chainer_bcnn/functions/accuracy/discrete_jaccard.py | from __future__ import absolute_import
import six
import numpy as np
import chainer
from chainer.backends import cuda
from chainer import function
from chainer.utils import type_check
from ..loss._helper import to_onehot
class DiscreteJaccard(function.Function):
""" Jaccard index
NOTE: This is not a differen... | 0.912204 | 0.328368 |
from manipulator import Manipulator
import numpy as np
class Transformer(Manipulator):
def __init__(self,df,metadataO):
metadata=metadataO.copy()
metadata = super().metadata_verifier(df,metadata)
tmp_metadata=self.remove_no_actions_per_type(metadata,'transformation')
super().__init__(... | src/pyroar/transformation/transformation.py | from manipulator import Manipulator
import numpy as np
class Transformer(Manipulator):
def __init__(self,df,metadataO):
metadata=metadataO.copy()
metadata = super().metadata_verifier(df,metadata)
tmp_metadata=self.remove_no_actions_per_type(metadata,'transformation')
super().__init__(... | 0.558327 | 0.343617 |
import argparse
import os
import time
import numpy as np
import cv2
from pobalog.hp_bar_recognition import HPBarRecognition
from pobalog.text_area_detection import TextAreaDetection
from pobalog.whole_image_matching import WholeImageMatching
def nothing(x):
pass
def run(video, screenshot_dir, engines):
cap... | pobalog/frame_input.py | import argparse
import os
import time
import numpy as np
import cv2
from pobalog.hp_bar_recognition import HPBarRecognition
from pobalog.text_area_detection import TextAreaDetection
from pobalog.whole_image_matching import WholeImageMatching
def nothing(x):
pass
def run(video, screenshot_dir, engines):
cap... | 0.343452 | 0.159774 |
import unittest
import sys
import random
import os
dirname, filename = os.path.split(os.path.abspath(__file__))
sys.path.append(dirname + '/..')
from src import populations
class TestPopulationGA(unittest.TestCase):
@classmethod
def setUpClass(cls):
def of1(*vars):
return vars[0] + vars... | unit_testing/test_populations_GA.py | import unittest
import sys
import random
import os
dirname, filename = os.path.split(os.path.abspath(__file__))
sys.path.append(dirname + '/..')
from src import populations
class TestPopulationGA(unittest.TestCase):
@classmethod
def setUpClass(cls):
def of1(*vars):
return vars[0] + vars... | 0.30013 | 0.488283 |
import os
import tempfile
import unittest
from telemetry.core import util
from telemetry.page import page
from telemetry.page import page_set
from telemetry.util import cloud_storage
class TestPageSet(unittest.TestCase):
def testServingDirs(self):
directory_path = tempfile.mkdtemp()
try:
ps = page_... | tools/telemetry/telemetry/page/page_set_unittest.py |
import os
import tempfile
import unittest
from telemetry.core import util
from telemetry.page import page
from telemetry.page import page_set
from telemetry.util import cloud_storage
class TestPageSet(unittest.TestCase):
def testServingDirs(self):
directory_path = tempfile.mkdtemp()
try:
ps = page_... | 0.38341 | 0.241087 |
from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class PackagesApi(object):
"""
NOTE: This class is auto generated by the swagger code ge... | bindings/python/src/cloudsmith_api/apis/packages_api.py | from __future__ import absolute_import
import sys
import os
import re
# python 2 and python 3 compatibility library
from six import iteritems
from ..configuration import Configuration
from ..api_client import ApiClient
class PackagesApi(object):
"""
NOTE: This class is auto generated by the swagger code ge... | 0.650467 | 0.0538 |
from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
from connect.processors_toolkit import find_by_id, merge
from connect.processors_toolkit.requests.assets import AssetBuilder
from connect.processors_toolkit.requests.helpers import make_param, request_model
from connect.processors_... | connect/processors_toolkit/requests/__init__.py | from __future__ import annotations
from typing import Any, Dict, List, Optional, Union
from connect.processors_toolkit import find_by_id, merge
from connect.processors_toolkit.requests.assets import AssetBuilder
from connect.processors_toolkit.requests.helpers import make_param, request_model
from connect.processors_... | 0.925731 | 0.11129 |
import datetime
import re
import bs4
import aiohttp
import isodate
import colors
import logger
import plugin_api
_logger = logger.LOGGER
class Plugin(plugin_api.LocalPlugin):
"""Youtube plugin"""
def help_msg(self):
return "displays youtube uri title"
@classmethod
async def _parse_youtub... | bot_modules/py/youtube.py | import datetime
import re
import bs4
import aiohttp
import isodate
import colors
import logger
import plugin_api
_logger = logger.LOGGER
class Plugin(plugin_api.LocalPlugin):
"""Youtube plugin"""
def help_msg(self):
return "displays youtube uri title"
@classmethod
async def _parse_youtub... | 0.454472 | 0.128498 |
import ctypes
import functools
from types import TracebackType
from typing import Optional
from typing import Type
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from pathlib import Path
__all__ = ('OppaiWrapper',)
class OppaiWrapper:
"""Lightweight wrapper around franc[e]sco's c89 oppai-ng library."""
... | tests/oppai_wrapper.py | import ctypes
import functools
from types import TracebackType
from typing import Optional
from typing import Type
from typing import TYPE_CHECKING
if TYPE_CHECKING:
from pathlib import Path
__all__ = ('OppaiWrapper',)
class OppaiWrapper:
"""Lightweight wrapper around franc[e]sco's c89 oppai-ng library."""
... | 0.809577 | 0.190536 |
from PySide2.QtWidgets import QWidget, QApplication, QTextEdit, QVBoxLayout, QPushButton
from PySide2.QtCore import QEventLoop
import sys
import threading
import asyncio
from concurrent import futures
import multiprocessing
import time
# Checking which blocks UI activity
class MainWindow(QWidget):
def __init__(... | Testbeds/asynchronousQtDemonstration.py | from PySide2.QtWidgets import QWidget, QApplication, QTextEdit, QVBoxLayout, QPushButton
from PySide2.QtCore import QEventLoop
import sys
import threading
import asyncio
from concurrent import futures
import multiprocessing
import time
# Checking which blocks UI activity
class MainWindow(QWidget):
def __init__(... | 0.323166 | 0.082475 |
import scipy.sparse as sp
from scipy.sparse import linalg
import numpy as np
import torch
import torch.nn as nn
from logging import getLogger
from libcity.model.abstract_traffic_state_model import AbstractTrafficStateModel
from libcity.model import loss
def calculate_normalized_laplacian(adj):
"""
L = D^-1/2 ... | libcity/model/traffic_speed_prediction/DCRNN.py | import scipy.sparse as sp
from scipy.sparse import linalg
import numpy as np
import torch
import torch.nn as nn
from logging import getLogger
from libcity.model.abstract_traffic_state_model import AbstractTrafficStateModel
from libcity.model import loss
def calculate_normalized_laplacian(adj):
"""
L = D^-1/2 ... | 0.825836 | 0.538619 |
from webob import Request, Response
import sys, logging, threading, random, urllib2
log = logging.getLogger(__name__)
def _make_test_app(test_op):
def app(env, sr):
req = Request(env)
log.debug('starting test operation: %s', test_op)
test_op(req)
log.debug('done')
r = Respo... | lib/WebOb-1.0.8/tests/test_in_wsgiref.py | from webob import Request, Response
import sys, logging, threading, random, urllib2
log = logging.getLogger(__name__)
def _make_test_app(test_op):
def app(env, sr):
req = Request(env)
log.debug('starting test operation: %s', test_op)
test_op(req)
log.debug('done')
r = Respo... | 0.235021 | 0.134804 |
import os
import sys
import json
import urllib.request
from docopt import docopt
args = docopt(__doc__)
def get_datasets():
# Starting dataset retrieval
print("\n\n##### get_datasets: Retrieving datasets")
if verbose: print(f"Running get_dataset with arguments: \n {args}")
assert input, "Empty input ... | src/get_datasets.py | import os
import sys
import json
import urllib.request
from docopt import docopt
args = docopt(__doc__)
def get_datasets():
# Starting dataset retrieval
print("\n\n##### get_datasets: Retrieving datasets")
if verbose: print(f"Running get_dataset with arguments: \n {args}")
assert input, "Empty input ... | 0.169784 | 0.23351 |
import itertools
from flask import request
from ggrc.models.cache import Cache
from ggrc.models.event import Event
from ggrc.models.revision import Revision
from ggrc.login import get_current_user_id
def _revision_generator(user_id, action, objects):
for obj in objects:
yield Revision(obj, user_id, action, ob... | src/ggrc/utils/log_event.py | import itertools
from flask import request
from ggrc.models.cache import Cache
from ggrc.models.event import Event
from ggrc.models.revision import Revision
from ggrc.login import get_current_user_id
def _revision_generator(user_id, action, objects):
for obj in objects:
yield Revision(obj, user_id, action, ob... | 0.657868 | 0.067026 |
import gym
import sonnet as snt
import tensorflow as tf
import numpy as np
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
def build_logits(action_space, latent_vec):
if isinstance(action_space, gym.spaces.Discrete):
return snt.Linear(output_size=action_space.n,
initializers... | rllib/models/doom_nature_model.py | import gym
import sonnet as snt
import tensorflow as tf
import numpy as np
from ray.rllib.models.tf.tf_modelv2 import TFModelV2
def build_logits(action_space, latent_vec):
if isinstance(action_space, gym.spaces.Discrete):
return snt.Linear(output_size=action_space.n,
initializers... | 0.900319 | 0.428831 |
import argparse
def str2bool(v):
return v.lower() in ('true')
def getParameters():
parser = argparse.ArgumentParser()
# general
parser.add_argument('--mode', type=str, default="test", choices=['train', 'finetune','test','debug'])
parser.add_argument('--cuda', type=int, default=0)
# training
... | parameters.py |
import argparse
def str2bool(v):
return v.lower() in ('true')
def getParameters():
parser = argparse.ArgumentParser()
# general
parser.add_argument('--mode', type=str, default="test", choices=['train', 'finetune','test','debug'])
parser.add_argument('--cuda', type=int, default=0)
# training
... | 0.357792 | 0.073132 |
import pandas as pd
import os
import re
from datapipeline.utils.loggers import Preprocessing_logger
from datapipeline.utils.io import (load_data, write_preprocessed_data,
create_file_object, file_object)
from datapipeline.utils.copy import copy_csv
def preprocess_c_trials (clinicalT):
"""
Descri... | src/datapipeline/preprocess/run.py | import pandas as pd
import os
import re
from datapipeline.utils.loggers import Preprocessing_logger
from datapipeline.utils.io import (load_data, write_preprocessed_data,
create_file_object, file_object)
from datapipeline.utils.copy import copy_csv
def preprocess_c_trials (clinicalT):
"""
Descri... | 0.370909 | 0.437343 |
""" Test for smoothing with kernels """
import numpy as np
from numpy.random import random_integers as randint
from ... import load_image
from ..kernel_smooth import LinearFilter, sigma2fwhm, fwhm2sigma
from ...externals.transforms3d.taitbryan import euler2mat
from ...core.api import Image, compose, AffineTransform, d... | nipy/algorithms/tests/test_kernel_smooth.py | """ Test for smoothing with kernels """
import numpy as np
from numpy.random import random_integers as randint
from ... import load_image
from ..kernel_smooth import LinearFilter, sigma2fwhm, fwhm2sigma
from ...externals.transforms3d.taitbryan import euler2mat
from ...core.api import Image, compose, AffineTransform, d... | 0.839832 | 0.72054 |
import numpy as np
import pyrotein as pr
import os
from loaddata import load_xlsx
# [[[ OBTAIN THE CONSENSUS SEQUENCE ]]]
# Read the sequence alignment result...
# [WARNING] !!!sequence alignment is not trustworthy, need to check manually
fl_aln = 'seq.align.fasta'
seq_dict = pr.fasta.read(fl_aln)
# Obtain the con... | examples/svd.full.py |
import numpy as np
import pyrotein as pr
import os
from loaddata import load_xlsx
# [[[ OBTAIN THE CONSENSUS SEQUENCE ]]]
# Read the sequence alignment result...
# [WARNING] !!!sequence alignment is not trustworthy, need to check manually
fl_aln = 'seq.align.fasta'
seq_dict = pr.fasta.read(fl_aln)
# Obtain the con... | 0.511717 | 0.251383 |
__author__ = """<NAME>"""
__email__ = '<EMAIL>'
__version__ = '0.2.2'
from typing import Optional, Type
import deta
from fastapi_users.manager import UserNotExists
from pydantic import UUID4
from fastapi_users.db.base import BaseUserDatabase
from fastapi_users.models import UD
class DetaBaseUserDatabase(BaseUserD... | fastapi_users_db_deta/__init__.py |
__author__ = """<NAME>"""
__email__ = '<EMAIL>'
__version__ = '0.2.2'
from typing import Optional, Type
import deta
from fastapi_users.manager import UserNotExists
from pydantic import UUID4
from fastapi_users.db.base import BaseUserDatabase
from fastapi_users.models import UD
class DetaBaseUserDatabase(BaseUserD... | 0.827026 | 0.102709 |
import torch
from torch import nn
import numpy as np
from .event_representation import EventRepresentation
from hats_pytorch import HATS
class HATSRepresentation(EventRepresentation):
def __init__(self, cfg, *args, **kwargs):
super().__init__(cfg, *args, **kwargs)
self.bins = cfg.bins
... | N-Caltech101/evrepr/representations/hats.py | import torch
from torch import nn
import numpy as np
from .event_representation import EventRepresentation
from hats_pytorch import HATS
class HATSRepresentation(EventRepresentation):
def __init__(self, cfg, *args, **kwargs):
super().__init__(cfg, *args, **kwargs)
self.bins = cfg.bins
... | 0.764935 | 0.329877 |
from docopt import docopt
import h5py
import logging
import warnings
warnings.filterwarnings('ignore',category=FutureWarning) # This supresses a warning caused by old TF version
import tensorflow as tf
import numpy as np
from tqdm import tqdm
tf.compat.v1.enable_eager_execution()
logging.basicConfig(level=logging.INF... | convert_tfrecord.py | from docopt import docopt
import h5py
import logging
import warnings
warnings.filterwarnings('ignore',category=FutureWarning) # This supresses a warning caused by old TF version
import tensorflow as tf
import numpy as np
from tqdm import tqdm
tf.compat.v1.enable_eager_execution()
logging.basicConfig(level=logging.INF... | 0.458106 | 0.260372 |
import math
import os
from decimal import Decimal
from django import template
from django.utils import timezone
from djmoney.money import Money
from app import settings
from app.enums import FileStatus
from app.utils import get_site_url
from event.enums import ApplicationStatus, DietType, TshirtSize, CompanyTier
from... | app/templatetags/app_tags.py | import math
import os
from decimal import Decimal
from django import template
from django.utils import timezone
from djmoney.money import Money
from app import settings
from app.enums import FileStatus
from app.utils import get_site_url
from event.enums import ApplicationStatus, DietType, TshirtSize, CompanyTier
from... | 0.359589 | 0.19046 |
from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
import math
class GraphConvolution(nn.Module):
"""
adapted from : https://github.com/tkipf/gcn/blob/92600c39797c2bfb61a508e52b88fb554df30... | model/GCN.py | from __future__ import absolute_import
from __future__ import print_function
import numpy as np
import torch.nn as nn
import torch
from torch.nn.parameter import Parameter
import math
class GraphConvolution(nn.Module):
"""
adapted from : https://github.com/tkipf/gcn/blob/92600c39797c2bfb61a508e52b88fb554df30... | 0.906581 | 0.326714 |
import numpy as np
import gym
import matplotlib.pyplot as plt
env = gym.make('CartPole-v0')
states = 4
dim = 2*states
number_actions = 2
epsilon = .5
alpha = .1
gamma = .9
lam = .5
MLR = .01
nrounds = 10000
def epsilongreedy(Qf, obs, epsilonf, num_actions):
actions = np.dot(Qf, obs)
if np.random.random() > ep... | RLbasics/Cart-addedfeatures.py | import numpy as np
import gym
import matplotlib.pyplot as plt
env = gym.make('CartPole-v0')
states = 4
dim = 2*states
number_actions = 2
epsilon = .5
alpha = .1
gamma = .9
lam = .5
MLR = .01
nrounds = 10000
def epsilongreedy(Qf, obs, epsilonf, num_actions):
actions = np.dot(Qf, obs)
if np.random.random() > ep... | 0.210766 | 0.606003 |
import logging
import numpy as np
from scipy.cluster.hierarchy import linkage, fcluster
from sklearn.cluster import dbscan
from scipy.stats import gmean
from article_separation.gnn.clustering.dbscan_textblocks import DBScanRelation
class TextblockClustering(object):
"""
class for textblock clustering
Usa... | article_separation/gnn/clustering/textblock_clustering.py | import logging
import numpy as np
from scipy.cluster.hierarchy import linkage, fcluster
from sklearn.cluster import dbscan
from scipy.stats import gmean
from article_separation.gnn.clustering.dbscan_textblocks import DBScanRelation
class TextblockClustering(object):
"""
class for textblock clustering
Usa... | 0.805364 | 0.26938 |
import os
import json
import sqlite3
from time import strftime, localtime
from index.neo4j import Neo4jToJson
from django.shortcuts import render
from django.http import HttpResponse
data_neo4j = Neo4jToJson()
# Create your views here.
def index(request):
data = {'nodes': [], 'links': []}
try:
data = ... | index/views.py | import os
import json
import sqlite3
from time import strftime, localtime
from index.neo4j import Neo4jToJson
from django.shortcuts import render
from django.http import HttpResponse
data_neo4j = Neo4jToJson()
# Create your views here.
def index(request):
data = {'nodes': [], 'links': []}
try:
data = ... | 0.10711 | 0.06134 |
from abc import abstractmethod
import torch
from flambe.metric.metric import Metric
class BinaryMetric(Metric):
def __init__(self, threshold: float = 0.5) -> None:
"""Initialize the Binary metric.
Parameters
---------
threshold: float
Given a probability p of belong... | flambe/metric/dev/binary.py | from abc import abstractmethod
import torch
from flambe.metric.metric import Metric
class BinaryMetric(Metric):
def __init__(self, threshold: float = 0.5) -> None:
"""Initialize the Binary metric.
Parameters
---------
threshold: float
Given a probability p of belong... | 0.972663 | 0.940024 |
load("//nixpkgs:nixpkgs.bzl", "nixpkgs_package")
load("//nixpkgs:private/constraints.bzl", "ensure_constraints")
_foreign_cc_nix_build = """
load("@rules_foreign_cc//toolchains/native_tools:native_tools_toolchain.bzl", "native_tool_toolchain")
filegroup(
name = "data",
srcs = glob(["bin/**"]),
)
native_tool_t... | nixpkgs/toolchains/foreign_cc.bzl | load("//nixpkgs:nixpkgs.bzl", "nixpkgs_package")
load("//nixpkgs:private/constraints.bzl", "ensure_constraints")
_foreign_cc_nix_build = """
load("@rules_foreign_cc//toolchains/native_tools:native_tools_toolchain.bzl", "native_tool_toolchain")
filegroup(
name = "data",
srcs = glob(["bin/**"]),
)
native_tool_t... | 0.360827 | 0.090454 |
from utils import *
from itertools import combinations
import numpy as np
import scipy.sparse as sp
from decagon.utility import preprocessing
from typing import Dict, NoReturn
from run_decagon import RunDecagon
class RunDecagonToy(RunDecagon):
"""
Decagon Runner on synthetic data.
Attributes
--------... | run_decagon_toy.py | from utils import *
from itertools import combinations
import numpy as np
import scipy.sparse as sp
from decagon.utility import preprocessing
from typing import Dict, NoReturn
from run_decagon import RunDecagon
class RunDecagonToy(RunDecagon):
"""
Decagon Runner on synthetic data.
Attributes
--------... | 0.879794 | 0.567098 |
def printComp(z) -> str:
return str(z.real) + "," + str(z.imag) + "\n"
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def leja():
for _i in range(18):
leja = []
with open(path + "data\\leja\\segment_" + str(_i) + ".txt", "r... | scripts/main.py | def printComp(z) -> str:
return str(z.real) + "," + str(z.imag) + "\n"
def file_len(fname):
with open(fname) as f:
for i, l in enumerate(f):
pass
return i + 1
def leja():
for _i in range(18):
leja = []
with open(path + "data\\leja\\segment_" + str(_i) + ".txt", "r... | 0.322526 | 0.164416 |
from http import HTTPStatus
from typing import Set, Type
# 3rd party
import docutils.nodes
import pytest
from apeye.requests_url import RequestsURL
from coincidence.params import count
from docutils import nodes
from docutils.utils import Reporter
from pytest_httpserver import HTTPServer
from sphinx.events import Even... | tests/test_github.py | from http import HTTPStatus
from typing import Set, Type
# 3rd party
import docutils.nodes
import pytest
from apeye.requests_url import RequestsURL
from coincidence.params import count
from docutils import nodes
from docutils.utils import Reporter
from pytest_httpserver import HTTPServer
from sphinx.events import Even... | 0.685634 | 0.297062 |
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class GetMariaDbServerResult:
"""
A collection of values returned by getMariaDbServer.
"""
def __init__(__self__, administrator_login=None, fqdn=None, id=None, location=None, name=... | sdk/python/pulumi_azure/mariadb/get_maria_db_server.py |
import json
import warnings
import pulumi
import pulumi.runtime
from typing import Union
from .. import utilities, tables
class GetMariaDbServerResult:
"""
A collection of values returned by getMariaDbServer.
"""
def __init__(__self__, administrator_login=None, fqdn=None, id=None, location=None, name=... | 0.664758 | 0.10316 |
import inetutils
import socket
import struct
import string
import os
IPVERSION = 4
IP_DF = 0x4000
IP_MF = 0x2000
IP_MAXPACKET = 65535
IPTOS_LOWDELAY = 0x10
IPTOS_THROUGHPUT = 0x08
IPTOS_RELIABILITY = 0x04
IPTOS_PREC_NETCONTROL = 0xe0
IPTOS_PREC_INTERNETCONTROL = 0xc0
IPTOS_PREC_CRITIC_ECP = 0xa0
IPTOS_PREC_FLASHOVER... | ip.py |
import inetutils
import socket
import struct
import string
import os
IPVERSION = 4
IP_DF = 0x4000
IP_MF = 0x2000
IP_MAXPACKET = 65535
IPTOS_LOWDELAY = 0x10
IPTOS_THROUGHPUT = 0x08
IPTOS_RELIABILITY = 0x04
IPTOS_PREC_NETCONTROL = 0xe0
IPTOS_PREC_INTERNETCONTROL = 0xc0
IPTOS_PREC_CRITIC_ECP = 0xa0
IPTOS_PREC_FLASHOVER... | 0.428831 | 0.161585 |
import json
from rest_framework import status
from rest_framework.test import APIClient, force_authenticate
from django.contrib.auth.models import User
from django.test import TestCase
from quickstart.models import Comment, Author, Post
from quickstart.serializers import CommentSerializer
from quickstart.tests.helper_t... | back-end/api/quickstart/tests/endpoints/test_comment.py | import json
from rest_framework import status
from rest_framework.test import APIClient, force_authenticate
from django.contrib.auth.models import User
from django.test import TestCase
from quickstart.models import Comment, Author, Post
from quickstart.serializers import CommentSerializer
from quickstart.tests.helper_t... | 0.475605 | 0.231147 |
import logging
import tabix
import biograph.variants as bgexvar
class VcfEntryInfo:
"""
Holds tracking information for a specific VCF entry while processing all the associated assemblies
call static method parse_vcf over a fetched region
"""
__slots__ = ["vcf_entry", "total_alleles", "alleles_r... | python/biograph/coverage/vcf_entry_info.py | import logging
import tabix
import biograph.variants as bgexvar
class VcfEntryInfo:
"""
Holds tracking information for a specific VCF entry while processing all the associated assemblies
call static method parse_vcf over a fetched region
"""
__slots__ = ["vcf_entry", "total_alleles", "alleles_r... | 0.676086 | 0.205037 |
import os
import copy
import yaml
import pandas as pd
from autumn.model_runner import build_model_runner
from autumn.tool_kit.scenarios import Scenario
from autumn.tool_kit.params import update_params
from datetime import date, timedelta
from apps.covid_19 import RegionApp
from apps.covid_19.mixing_optimisation.cons... | apps/covid_19/mixing_optimisation/mixing_opti.py | import os
import copy
import yaml
import pandas as pd
from autumn.model_runner import build_model_runner
from autumn.tool_kit.scenarios import Scenario
from autumn.tool_kit.params import update_params
from datetime import date, timedelta
from apps.covid_19 import RegionApp
from apps.covid_19.mixing_optimisation.cons... | 0.503906 | 0.292103 |
import os
import dq0.sdk
from dq0.sdk.data.utils import plotting, util
def preprocess_dataset(data_source):
column_names_list = [
'lastname',
'firstname',
'age',
'workclass',
'fnlwgt',
'education',
'education-num',
'marital-status',
'occupa... | dq0/sdk/examples/census/eda.py | import os
import dq0.sdk
from dq0.sdk.data.utils import plotting, util
def preprocess_dataset(data_source):
column_names_list = [
'lastname',
'firstname',
'age',
'workclass',
'fnlwgt',
'education',
'education-num',
'marital-status',
'occupa... | 0.648132 | 0.35883 |
import logging
from pathvalidate import sanitize_filename
from pathlib import Path
from .utils import validate_url, write_details
from .utils import download as download_file
from .errors import InvalidURL
from .fetcher import *
from .manga import Manga
from .chapter import Chapter
from .downloader import ChapterPageDo... | mangadex_downloader/main.py | import logging
from pathvalidate import sanitize_filename
from pathlib import Path
from .utils import validate_url, write_details
from .utils import download as download_file
from .errors import InvalidURL
from .fetcher import *
from .manga import Manga
from .chapter import Chapter
from .downloader import ChapterPageDo... | 0.759225 | 0.169269 |
import numpy as np
from Supervised.Regression.RegressionTree import regress_tree_utils
def prune(tree, testData):
"""
Desc:
从上而下找到叶节点，用测试数据集来判断将这些叶节点合并是否能降低测试误差
Args:
tree -- 待剪枝的树
testData -- 剪枝所需要的测试数据 testData
Returns:
tree -- 剪枝完成的树
"""
# 判断是否测试数据集没有数据，如果没... | Supervised/Regression/RegressionTree/rt_prune.py | import numpy as np
from Supervised.Regression.RegressionTree import regress_tree_utils
def prune(tree, testData):
"""
Desc:
从上而下找到叶节点，用测试数据集来判断将这些叶节点合并是否能降低测试误差
Args:
tree -- 待剪枝的树
testData -- 剪枝所需要的测试数据 testData
Returns:
tree -- 剪枝完成的树
"""
# 判断是否测试数据集没有数据，如果没... | 0.467332 | 0.620075 |
import tensorflow as tf
import os
import pandas as pd
import functools
import random
LABEL_FILE = 'train.csv'
IMAGE_SHAPE = [224, 224]
NB_EPOCHS = None
NUM_PARALLEL_CALLS = 4
PREFETCH_BUFFER_SIZE = 2 ** 8
SHUFFLE_BUFFER_SIZE = 2 ** 8
def get_landmark_challange_generator(data_dir, is_training=True):
"""
Args:
dat... | data_generation/data_generator.py | import tensorflow as tf
import os
import pandas as pd
import functools
import random
LABEL_FILE = 'train.csv'
IMAGE_SHAPE = [224, 224]
NB_EPOCHS = None
NUM_PARALLEL_CALLS = 4
PREFETCH_BUFFER_SIZE = 2 ** 8
SHUFFLE_BUFFER_SIZE = 2 ** 8
def get_landmark_challange_generator(data_dir, is_training=True):
"""
Args:
dat... | 0.47171 | 0.258262 |
import caffe
import re
from pittnuts import *
import os
import matplotlib.pyplot as plt
import argparse
import caffeparser
import caffe_apps
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--original_alexnet', type=str, required=True,help="The original alexnet with group.")
... | python/group_spitter.py | import caffe
import re
from pittnuts import *
import os
import matplotlib.pyplot as plt
import argparse
import caffeparser
import caffe_apps
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--original_alexnet', type=str, required=True,help="The original alexnet with group.")
... | 0.269806 | 0.102754 |
import config
from api import send_message
from reborn import is_sudoer
def on_msg_received(msg, matches):
# Checa se o usuário tem permissão e tem a quantidade correta de args no comando
command = matches.group(2)
plugin = matches.group(3)
if command is None and plugin is None:
string = ""
... | plugins/plugins.py | import config
from api import send_message
from reborn import is_sudoer
def on_msg_received(msg, matches):
# Checa se o usuário tem permissão e tem a quantidade correta de args no comando
command = matches.group(2)
plugin = matches.group(3)
if command is None and plugin is None:
string = ""
... | 0.202996 | 0.197406 |
import random
import sys
import argparse
from de_sim.simulation_message import SimulationMessage
from de_sim.simulation_object import ApplicationSimulationObject
from de_sim.simulation_engine import SimulationEngine
from de_sim.examples.debug_logs import logs
def obj_name(obj_num):
# create object name from inde... | de_sim/examples/phold.py | import random
import sys
import argparse
from de_sim.simulation_message import SimulationMessage
from de_sim.simulation_object import ApplicationSimulationObject
from de_sim.simulation_engine import SimulationEngine
from de_sim.examples.debug_logs import logs
def obj_name(obj_num):
# create object name from inde... | 0.475362 | 0.36886 |
from bota.utility.db_utility import BotaDB
from bota import db_constants as dbc
from bota.constant import DEFAULT_PREFIX
bota_db = BotaDB(host=dbc.HOST_NAME, database=dbc.DATABASE_NAME,
user=dbc.USER, password=dbc.PASSWORD)
class GuildCaller:
def __init__(self):
pass
def get_gu... | bota/guild_process/guild_main.py | from bota.utility.db_utility import BotaDB
from bota import db_constants as dbc
from bota.constant import DEFAULT_PREFIX
bota_db = BotaDB(host=dbc.HOST_NAME, database=dbc.DATABASE_NAME,
user=dbc.USER, password=dbc.PASSWORD)
class GuildCaller:
def __init__(self):
pass
def get_gu... | 0.344885 | 0.065425 |
import torch
import torch.nn as nn
import random
from transformers import (
ElectraConfig,
ElectraTokenizer
)
from model.koelectra import koElectraForSequenceClassification,koelectra_input
from model.kobert import KoBERTforSequenceClassfication, kobert_input
from kobert_transformers import get_tokenizer
class Dia... | ai/chatbot/service/module.py | import torch
import torch.nn as nn
import random
from transformers import (
ElectraConfig,
ElectraTokenizer
)
from model.koelectra import koElectraForSequenceClassification,koelectra_input
from model.kobert import KoBERTforSequenceClassfication, kobert_input
from kobert_transformers import get_tokenizer
class Dia... | 0.582847 | 0.27488 |
import pytz
from sqlalchemy import Column, Integer, String, DateTime, Interval, ForeignKey, UniqueConstraint
from sqlalchemy.orm import relationship
from .base import Base
class Flight(Base):
"""
Class representing a numbered flight for a named airline on a given date and at a given time
"""
__tablena... | src/flight_model/model/flight.py | import pytz
from sqlalchemy import Column, Integer, String, DateTime, Interval, ForeignKey, UniqueConstraint
from sqlalchemy.orm import relationship
from .base import Base
class Flight(Base):
"""
Class representing a numbered flight for a named airline on a given date and at a given time
"""
__tablena... | 0.847179 | 0.474022 |
from snakemake.io import expand
from snakemake.exceptions import WildcardError
import pytest
def test_simple_expand():
# single filepattern
assert expand("{a}.out", a="test") == ["test.out"]
# multiple filepatterns
assert expand(["{a}.out", "{b}.out"], a="a", b="b") == ["a.out", "b.out"]
# multipl... | tests/test_expand.py | from snakemake.io import expand
from snakemake.exceptions import WildcardError
import pytest
def test_simple_expand():
# single filepattern
assert expand("{a}.out", a="test") == ["test.out"]
# multiple filepatterns
assert expand(["{a}.out", "{b}.out"], a="a", b="b") == ["a.out", "b.out"]
# multipl... | 0.787686 | 0.754915 |
"""Evaluates a network on a dataset and writes the result in an npy file."""
import os
import pickle
import time
from absl import app
from absl import flags
import apache_beam as beam
# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
from ldif.inference import e... | ldif/results/make_results.py | """Evaluates a network on a dataset and writes the result in an npy file."""
import os
import pickle
import time
from absl import app
from absl import flags
import apache_beam as beam
# LDIF is an internal package, should be imported last.
# pylint: disable=g-bad-import-order
from ldif.inference import e... | 0.521471 | 0.294773 |
#------------------------------------------------------------------------------
# module imports
#------------------------------------------------------------------------------
import sys
import json
import tftpy
import socket
import select
#----------------------------------------------------------------------------... | utilities/TDK_Automation_Scripts/python-lib/consoleLogTransfer.py |
#------------------------------------------------------------------------------
# module imports
#------------------------------------------------------------------------------
import sys
import json
import tftpy
import socket
import select
#----------------------------------------------------------------------------... | 0.207696 | 0.048519 |
from incuna_test_utils.compat import DJANGO_LT_16, Python2AssertMixin
from incuna_test_utils.testcases.request import BaseRequestTestCase
import mock
from . import factories
from .. import views
from videos.module.chapters.tests.factories import ChapterFactory
from videos.module.speakers.tests.factories import Speaker... | videos/tests/test_views.py | from incuna_test_utils.compat import DJANGO_LT_16, Python2AssertMixin
from incuna_test_utils.testcases.request import BaseRequestTestCase
import mock
from . import factories
from .. import views
from videos.module.chapters.tests.factories import ChapterFactory
from videos.module.speakers.tests.factories import Speaker... | 0.695545 | 0.393909 |
import argparse
import os
import dgl
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl import load_graphs
from models import dummy_gnn_model, GCNII
from NodeExplainerModule import NodeExplainerModule
from utils_graph import extract_subgraph, visualize_sub_graph
def main(args):
#... | examples/pytorch/gnn_explainer/explain_main.py |
import argparse
import os
import dgl
import torch as th
import torch.nn as nn
import torch.nn.functional as F
from dgl import load_graphs
from models import dummy_gnn_model, GCNII
from NodeExplainerModule import NodeExplainerModule
from utils_graph import extract_subgraph, visualize_sub_graph
def main(args):
#... | 0.832441 | 0.293803 |
import hashlib
import os
__copyright__ = '2015, JSK Lab'
__author__ = '<NAME> <<EMAIL>>'
__version__ = '0.1'
def rst_image_table_data(url_prefix, img_files):
N_COLUMN = 3
# collect label and blocks
labels = []
blocks = []
for i, img_file in enumerate(img_files):
title, _ = os.path.splite... | jsk_recognition/doc/add_img_tables_to_index.py | import hashlib
import os
__copyright__ = '2015, JSK Lab'
__author__ = '<NAME> <<EMAIL>>'
__version__ = '0.1'
def rst_image_table_data(url_prefix, img_files):
N_COLUMN = 3
# collect label and blocks
labels = []
blocks = []
for i, img_file in enumerate(img_files):
title, _ = os.path.splite... | 0.186836 | 0.101189 |
from pyomo.environ import *
model = m = ConcreteModel()
m.x1 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x2 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x3 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x4 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x5 = Var(within=Reals,bounds=(0,None),ini... | tests/examples/minlplib/st_m2.py |
from pyomo.environ import *
model = m = ConcreteModel()
m.x1 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x2 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x3 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x4 = Var(within=Reals,bounds=(0,None),initialize=0)
m.x5 = Var(within=Reals,bounds=(0,None),ini... | 0.413951 | 0.212845 |
from pretraining.utils import *
import numpy as np
from pretraining.oov import readParam
from collections import defaultdict
import operator
posParser = {'n': 'NN', 'a': 'JJ', 's': 'JJ', 'v': 'VB', 'r': 'RB'}
pos = ['VB', 'NN', 'RB', 'JJ']
def load_gre_test(path):
gre = []
with open(path, 'r') as f:
f... | bptf/gre_test.py | from pretraining.utils import *
import numpy as np
from pretraining.oov import readParam
from collections import defaultdict
import operator
posParser = {'n': 'NN', 'a': 'JJ', 's': 'JJ', 'v': 'VB', 'r': 'RB'}
pos = ['VB', 'NN', 'RB', 'JJ']
def load_gre_test(path):
gre = []
with open(path, 'r') as f:
f... | 0.214691 | 0.33764 |
import argparse
import sqlite3
from flask import Flask, jsonify, send_from_directory
from flask_cors import CORS
# http://blog.miguelgrinberg.com/post/designing-a-restful-api-with-python-and-flask
from werkzeug.utils import redirect
app = Flask(__name__)
CORS(app)
def read_calling_points(all_points):
points = [... | railserver.py | import argparse
import sqlite3
from flask import Flask, jsonify, send_from_directory
from flask_cors import CORS
# http://blog.miguelgrinberg.com/post/designing-a-restful-api-with-python-and-flask
from werkzeug.utils import redirect
app = Flask(__name__)
CORS(app)
def read_calling_points(all_points):
points = [... | 0.47658 | 0.209672 |
import warnings
from sklearn.metrics import mean_squared_error
from sklearn.model_selection._split import train_test_split
import traceback
from unsupervised_learning.src import plot_clusters
import copy
warnings.filterwarnings("ignore")
import os
os.chdir(os.path.dirname(__file__))
from sklearn.metrics.pair... | unsupervised_learning/src/assignment3_script.py | import warnings
from sklearn.metrics import mean_squared_error
from sklearn.model_selection._split import train_test_split
import traceback
from unsupervised_learning.src import plot_clusters
import copy
warnings.filterwarnings("ignore")
import os
os.chdir(os.path.dirname(__file__))
from sklearn.metrics.pair... | 0.279533 | 0.300797 |
import logging
import gspread
import datetime
from django.conf import settings
from .models import Cleanup
logger = logging.getLogger('cleanups.google_sheets')
# TODO: Remove variables and use settings directly.
GOOGLE_SHEETS_VALIDATION = settings.GOOGLE_SHEETS_VALIDATION
GOOGLE_SHEETS_SCOPE = settings.GOOGLE_SHEE... | trashtalk/apps/integrations/google_sheets/google_sheets.py | import logging
import gspread
import datetime
from django.conf import settings
from .models import Cleanup
logger = logging.getLogger('cleanups.google_sheets')
# TODO: Remove variables and use settings directly.
GOOGLE_SHEETS_VALIDATION = settings.GOOGLE_SHEETS_VALIDATION
GOOGLE_SHEETS_SCOPE = settings.GOOGLE_SHEE... | 0.193414 | 0.099996 |
import argparse
from collections import defaultdict
import cv2, os
from pytorch_toolbelt.inference.tiles import ImageSlicer
from pytorch_toolbelt.utils import fs
from pytorch_toolbelt.utils.fs import id_from_fname, read_image_as_is
import pandas as pd
from tqdm import tqdm
from inria.dataset import TRAIN_LOCATIONS, r... | make_train_tiles.py | import argparse
from collections import defaultdict
import cv2, os
from pytorch_toolbelt.inference.tiles import ImageSlicer
from pytorch_toolbelt.utils import fs
from pytorch_toolbelt.utils.fs import id_from_fname, read_image_as_is
import pandas as pd
from tqdm import tqdm
from inria.dataset import TRAIN_LOCATIONS, r... | 0.470007 | 0.285702 |
import datetime
import json
import sqlalchemy
from sqlalchemy import and_, desc, func, or_, distinct
from rdr_service.census_regions import census_regions
from rdr_service.code_constants import PPI_SYSTEM
from rdr_service.dao.base_dao import BaseDao, UpdatableDao
from rdr_service.dao.code_dao import CodeDao
from rdr_... | rdr_service/dao/metrics_cache_dao.py | import datetime
import json
import sqlalchemy
from sqlalchemy import and_, desc, func, or_, distinct
from rdr_service.census_regions import census_regions
from rdr_service.code_constants import PPI_SYSTEM
from rdr_service.dao.base_dao import BaseDao, UpdatableDao
from rdr_service.dao.code_dao import CodeDao
from rdr_... | 0.397938 | 0.132458 |
from unittest.mock import Mock, patch
import pytest
from graph_structures_pb2 import Client, Node, NodeType, Status, VirtualNode
import ujt.constants
import ujt.transformers
@pytest.fixture
def patch_path():
return "ujt.transformers"
def test_apply_node_property_classes(assert_same_elements):
node_name =... | tests/test_transformers.py |
from unittest.mock import Mock, patch
import pytest
from graph_structures_pb2 import Client, Node, NodeType, Status, VirtualNode
import ujt.constants
import ujt.transformers
@pytest.fixture
def patch_path():
return "ujt.transformers"
def test_apply_node_property_classes(assert_same_elements):
node_name =... | 0.706798 | 0.535159 |
import datetime
import io
import json
import logging
import re
import boto3
import xlwt
from botocore.exceptions import ClientError
from jinja2 import Template
from xlwt.compat import basestring
from constant import Constant
from me_logger import log_error
from util import get_accounts_by_company_name, get_all_accoun... | src/reports.py | import datetime
import io
import json
import logging
import re
import boto3
import xlwt
from botocore.exceptions import ClientError
from jinja2 import Template
from xlwt.compat import basestring
from constant import Constant
from me_logger import log_error
from util import get_accounts_by_company_name, get_all_accoun... | 0.378459 | 0.087252 |
from Adafruit_LED_Backpack import AlphaNum4, BicolorBargraph24, SevenSegment
import widget
import RPi.GPIO as GPIO
from PIL import Image, ImageDraw, ImageFont
import cups
import datetime
import time
class Timer(object):
"""A simple timer class"""
def __init__(self):
pass
def start(self):
... | ParkTimer.python/parktimer.py | from Adafruit_LED_Backpack import AlphaNum4, BicolorBargraph24, SevenSegment
import widget
import RPi.GPIO as GPIO
from PIL import Image, ImageDraw, ImageFont
import cups
import datetime
import time
class Timer(object):
"""A simple timer class"""
def __init__(self):
pass
def start(self):
... | 0.669961 | 0.2667 |
from functools import partial
from enum import Enum
import logging
import serial
class Protocol(Enum):
UNKNOWN = 0
NEC = 1
SONY = 2
NECX = 7
NUM_N_BYTES = 4
class Ack(object):
def __init__(self, label, nac_info=None):
self.label = label
self.nac_info = nac_info
def __bool... | python/ser_com.py | from functools import partial
from enum import Enum
import logging
import serial
class Protocol(Enum):
UNKNOWN = 0
NEC = 1
SONY = 2
NECX = 7
NUM_N_BYTES = 4
class Ack(object):
def __init__(self, label, nac_info=None):
self.label = label
self.nac_info = nac_info
def __bool... | 0.516839 | 0.110904 |
import json
import math
#loads the give file location and creates a lootTable object
def load(f):
fp = open(f, 'r')
return lootTable(f,json.load(fp))
#saves the provided lootTable object at the provided location
def save(f, table):
fp = open(f,'w')
json.dump(table.convert_to_dict(), fp, indent=2)
def... | loot_table.py | import json
import math
#loads the give file location and creates a lootTable object
def load(f):
fp = open(f, 'r')
return lootTable(f,json.load(fp))
#saves the provided lootTable object at the provided location
def save(f, table):
fp = open(f,'w')
json.dump(table.convert_to_dict(), fp, indent=2)
def... | 0.254602 | 0.09645 |
import pyttsx3
import speech_recognition as sr
import re
import sys
from webscraper import Scraper
# pyaudio
scraper = Scraper()
# RegEx Patterns
BASIC_PATTERNS = {
re.compile("[\w\s]+ (cases|case)"): scraper.get_cases,
re.compile("[\w\s]+ (cases|case) worldwide"): scraper.get_cases,
re.compile("[\w\s]+ ... | voice_assistant.py | import pyttsx3
import speech_recognition as sr
import re
import sys
from webscraper import Scraper
# pyaudio
scraper = Scraper()
# RegEx Patterns
BASIC_PATTERNS = {
re.compile("[\w\s]+ (cases|case)"): scraper.get_cases,
re.compile("[\w\s]+ (cases|case) worldwide"): scraper.get_cases,
re.compile("[\w\s]+ ... | 0.128361 | 0.304494 |
import torch.nn as nn
from segmentation_models_pytorch.unet.decoder import CenterBlock, DecoderBlock
class UnetDecoderClipped(nn.Module):
def __init__(
self,
encoder_channels,
decoder_channels,
decoder_blocks=5,
use_batchnorm=True,
attention_t... | multitask_lightning/models/decoder.py | import torch.nn as nn
from segmentation_models_pytorch.unet.decoder import CenterBlock, DecoderBlock
class UnetDecoderClipped(nn.Module):
def __init__(
self,
encoder_channels,
decoder_channels,
decoder_blocks=5,
use_batchnorm=True,
attention_t... | 0.937769 | 0.39004 |
# [3rd party modules]
import numpy as np
import sys
import time
import pickle
from modules.utils import my_print
import matplotlib.pyplot as plt
try:
import mujoco_py as mjPy
except ImportError as e:
raise error.DependencyNotInstalled( "{}. (HINT: you need to install mujoco_py, \
... | MuJoCo/modules/controllers.py |
# [3rd party modules]
import numpy as np
import sys
import time
import pickle
from modules.utils import my_print
import matplotlib.pyplot as plt
try:
import mujoco_py as mjPy
except ImportError as e:
raise error.DependencyNotInstalled( "{}. (HINT: you need to install mujoco_py, \
... | 0.289472 | 0.387574 |
from scipy.spatial import Delaunay
from collections import defaultdict
from itertools import combinations
import numpy as np
from memly.metrics import Metric
class Domains(Metric):
def __init__(self, membrane, title="Domains", units="unitless"):
"""
Detects lipid domains using Delaunay triangulat... | memly/domains.py | from scipy.spatial import Delaunay
from collections import defaultdict
from itertools import combinations
import numpy as np
from memly.metrics import Metric
class Domains(Metric):
def __init__(self, membrane, title="Domains", units="unitless"):
"""
Detects lipid domains using Delaunay triangulat... | 0.767123 | 0.536738 |
import doxygen_parsing
import inspect
import string
import os
import sys
import comment_verifier
comment_verifier.check(inspect.getdoc(doxygen_parsing.someFunction),
"The function comment")
comment_verifier.check(inspect.getdoc(doxygen_parsing.SomeClass),
"The class comment")
comment_verifier.check(inspect.get... | cacreader/swig-4.0.2/Examples/test-suite/python/doxygen_parsing_runme.py | import doxygen_parsing
import inspect
import string
import os
import sys
import comment_verifier
comment_verifier.check(inspect.getdoc(doxygen_parsing.someFunction),
"The function comment")
comment_verifier.check(inspect.getdoc(doxygen_parsing.SomeClass),
"The class comment")
comment_verifier.check(inspect.get... | 0.408985 | 0.226377 |
import contextlib
import logging
import random
from flask import request, g
from sqlalchemy import create_engine as sqlalchemy_create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import scoped_session, sessionmaker
from app import app, load_app_config
from app.exc import GulDanEx... | app/db.py | import contextlib
import logging
import random
from flask import request, g
from sqlalchemy import create_engine as sqlalchemy_create_engine
from sqlalchemy.ext.declarative import declarative_base
from sqlalchemy.orm import scoped_session, sessionmaker
from app import app, load_app_config
from app.exc import GulDanEx... | 0.348867 | 0.075346 |