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 SimPy.Simulation import *
import random
import numpy
import math
import matplotlib.pyplot as pl
## Useful extras ----------
def conf(L):
"""confidence interval"""
lower = numpy.mean(L) - 1.96*numpy.std(L)/math.sqrt(len(L))
upper = numpy.mean(L) + 1.96*numpy.std(L)/math.sqrt(len(L))
return (lower... | Undergraduate/COMP312/Final Project Submission/.fr-SjIgVy/project/Scripts and Data/MMC.py |
from SimPy.Simulation import *
import random
import numpy
import math
import matplotlib.pyplot as pl
## Useful extras ----------
def conf(L):
"""confidence interval"""
lower = numpy.mean(L) - 1.96*numpy.std(L)/math.sqrt(len(L))
upper = numpy.mean(L) + 1.96*numpy.std(L)/math.sqrt(len(L))
return (lower... | 0.529263 | 0.420183 |
from urllib.parse import urlencode
import requests
from typeform import exception
from typeform.enumerator import ErrorEnum
class Client(object):
BASE_URL = 'https://api.typeform.com/'
def __init__(self, client_id, client_secret):
self.client_id = client_id
self.client_secret = client_secre... | typeform/client.py | from urllib.parse import urlencode
import requests
from typeform import exception
from typeform.enumerator import ErrorEnum
class Client(object):
BASE_URL = 'https://api.typeform.com/'
def __init__(self, client_id, client_secret):
self.client_id = client_id
self.client_secret = client_secre... | 0.779657 | 0.16238 |
import os
import discord
import random
from discord.ext import commands
from dotenv import load_dotenv
load_dotenv()
TOKEN = os.getenv('DISCORD_TOKEN')
client = discord.Client()
#######################
# Twitch notification #
#######################
############
# Commands #
############
client = ... | main.py | import os
import discord
import random
from discord.ext import commands
from dotenv import load_dotenv
load_dotenv()
TOKEN = os.getenv('DISCORD_TOKEN')
client = discord.Client()
#######################
# Twitch notification #
#######################
############
# Commands #
############
client = ... | 0.249082 | 0.038358 |
from django.views import View
from django.contrib import messages
from django.shortcuts import render, redirect
from devices.controllers import cisco_controller as controller
from devices.models import *
class InterfaceDetails(View):
template = 'device_interface.html'
success_redirect = 'devices:Interface-Det... | NetManager/devices/views/interface.py | from django.views import View
from django.contrib import messages
from django.shortcuts import render, redirect
from devices.controllers import cisco_controller as controller
from devices.models import *
class InterfaceDetails(View):
template = 'device_interface.html'
success_redirect = 'devices:Interface-Det... | 0.638948 | 0.05634 |
import sys
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import QApplication,QMainWindow
from PyQt5.QtWidgets import *
from Ui_login import Ui_Login
from Ui_register import Ui_Register
from DataBase import DataBase
from Ui_tablelist import Ui_TableList
from Ui_products import Ui_MainWindow
... | pyqt数据库系统/仓库管理/Ui_main.py | import sys
from PyQt5.QtCore import *
from PyQt5.QtGui import *
from PyQt5.QtWidgets import QApplication,QMainWindow
from PyQt5.QtWidgets import *
from Ui_login import Ui_Login
from Ui_register import Ui_Register
from DataBase import DataBase
from Ui_tablelist import Ui_TableList
from Ui_products import Ui_MainWindow
... | 0.077252 | 0.048092 |
import copy
from datetime import datetime, timezone
from logging import getLogger
from typing import Dict, Iterable, Optional, Type, Union, Set, List
from ogr.abstract import GitProject
from packit.config import JobConfigTriggerType, PackageConfig
from packit_service.config import PackageConfigGetter, ServiceConfig
f... | packit_service/worker/events/event.py | import copy
from datetime import datetime, timezone
from logging import getLogger
from typing import Dict, Iterable, Optional, Type, Union, Set, List
from ogr.abstract import GitProject
from packit.config import JobConfigTriggerType, PackageConfig
from packit_service.config import PackageConfigGetter, ServiceConfig
f... | 0.76238 | 0.226612 |
import uuid
from msrest.pipeline import ClientRawResponse
from msrestazure.azure_exceptions import CloudError
from .. import models
class AlertsOperations(object):
"""AlertsOperations operations.
You should not instantiate directly this class, but create a Client instance that will create it for you and at... | sdk/security/azure-mgmt-security/azure/mgmt/security/operations/_alerts_operations.py |
import uuid
from msrest.pipeline import ClientRawResponse
from msrestazure.azure_exceptions import CloudError
from .. import models
class AlertsOperations(object):
"""AlertsOperations operations.
You should not instantiate directly this class, but create a Client instance that will create it for you and at... | 0.808483 | 0.109182 |
import os
import numpy as np
from PIL import Image # pylint: disable=E0401
import tensorflow.compat.v1 as tf # pylint: disable=C0411, E0401
# Import AMCT toolkit and set log level. There are four log levels,
# 'info', 'debug', 'warning' and 'error'. The default level is 'info'.
import amct_tensorflow as amct # pylint:... | python/level1_single_api/9_amct/amct_tensorflow/resnet-50_v1/src/resnet-50_v1_nuq.py | import os
import numpy as np
from PIL import Image # pylint: disable=E0401
import tensorflow.compat.v1 as tf # pylint: disable=C0411, E0401
# Import AMCT toolkit and set log level. There are four log levels,
# 'info', 'debug', 'warning' and 'error'. The default level is 'info'.
import amct_tensorflow as amct # pylint:... | 0.402627 | 0.213551 |
import math
import random
import sys
class gen:
"""条件式のジェネレータ"""
# ランダムでループを回しても operators と sentences の位置を同期する
op_index = [0, 1, 2, 3, 4, 5]
# python の比較演算子リスト（問題集として利用するには、' == ' と ' != ' をそれぞれ '=', '<>' に変換する）
operators = {
0: ' == ', 1: ' != ', 2: ' < ', 3: ' > ', 4: ' <= ',... | create_drills.py | import math
import random
import sys
class gen:
"""条件式のジェネレータ"""
# ランダムでループを回しても operators と sentences の位置を同期する
op_index = [0, 1, 2, 3, 4, 5]
# python の比較演算子リスト（問題集として利用するには、' == ' と ' != ' をそれぞれ '=', '<>' に変換する）
operators = {
0: ' == ', 1: ' != ', 2: ' < ', 3: ' > ', 4: ' <= ',... | 0.154919 | 0.422683 |
import logging
import os
import shutil
import sys
import GPy
import numpy as np
import pandas as pd
import seaborn as sb
from config import Config
from controllers import sin_controller_02D
from dynamics import dynamics_traj, duffing_dynamics, pendulum_dynamics, \
VanderPol_dynamics, duffing_dynamics_discrete, \
... | quasilinear_observer_GP.py | import logging
import os
import shutil
import sys
import GPy
import numpy as np
import pandas as pd
import seaborn as sb
from config import Config
from controllers import sin_controller_02D
from dynamics import dynamics_traj, duffing_dynamics, pendulum_dynamics, \
VanderPol_dynamics, duffing_dynamics_discrete, \
... | 0.583915 | 0.251223 |
from contextlib import contextmanager
from capybara.selector.expression_filter import ExpressionFilter
from capybara.selector.node_filter import NodeFilter
filter_sets = {}
# Dict[str, FilterSet]: A dictionary of global filter sets keyed by name.
class FilterSet(object):
"""
A group of filters for use by :... | capybara/selector/filter_set.py | from contextlib import contextmanager
from capybara.selector.expression_filter import ExpressionFilter
from capybara.selector.node_filter import NodeFilter
filter_sets = {}
# Dict[str, FilterSet]: A dictionary of global filter sets keyed by name.
class FilterSet(object):
"""
A group of filters for use by :... | 0.89751 | 0.288657 |
class ModelItemBase(object):
def __init__(self, **kwargs):
for k in kwargs.keys():
setattr(self, k, kwargs[k])
class IngredientTypeDef(ModelItemBase):
def get_default_unit(self):
return self.get_all_units()[0]
def get_all_units(self):
if self.extends is not None:
... | recipec/mm_classes.py | class ModelItemBase(object):
def __init__(self, **kwargs):
for k in kwargs.keys():
setattr(self, k, kwargs[k])
class IngredientTypeDef(ModelItemBase):
def get_default_unit(self):
return self.get_all_units()[0]
def get_all_units(self):
if self.extends is not None:
... | 0.635675 | 0.218795 |
from django.test import TestCase
from djangojs.utils import StorageGlobber
from os.path import normpath
class StorageGlobberTest(TestCase):
def test_no_js_file(self):
'''Should handle empty js file list'''
self.assertEqual(StorageGlobber.glob(), [])
def test_single_js_file(self):
''... | myenv/lib/python3.5/site-packages/djangojs/tests/test_globber.py | from django.test import TestCase
from djangojs.utils import StorageGlobber
from os.path import normpath
class StorageGlobberTest(TestCase):
def test_no_js_file(self):
'''Should handle empty js file list'''
self.assertEqual(StorageGlobber.glob(), [])
def test_single_js_file(self):
''... | 0.669529 | 0.40251 |
import unittest
class CaseLetters(unittest.TestCase):
def testA(self): self.assertEquals("A" + "A", "AA")
def testB(self): self.assertEquals("B" + "B", "BB")
def testC(self): self.assertEquals("C" + "C", "CC")
def testD(self): self.assertEquals("D" + "D", "DD")
def testE(self): self.assertEquals("E... | tests/suites.py | import unittest
class CaseLetters(unittest.TestCase):
def testA(self): self.assertEquals("A" + "A", "AA")
def testB(self): self.assertEquals("B" + "B", "BB")
def testC(self): self.assertEquals("C" + "C", "CC")
def testD(self): self.assertEquals("D" + "D", "DD")
def testE(self): self.assertEquals("E... | 0.545528 | 0.511717 |
import random
import apiai
import json
import cv2
import os
import speech_recognition as sr
def recognize_face():
while True:
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, ... | main.py | import random
import apiai
import json
import cv2
import os
import speech_recognition as sr
def recognize_face():
while True:
ret, frame = cap.read()
# Our operations on the frame come here
gray = cv2.cvtColor(frame, cv2.COLOR_BGR2GRAY)
faces = face_cascade.detectMultiScale(gray, ... | 0.295027 | 0.21893 |
import json
import logging
import pytest
from lifemonitor.api import models
from tests import utils
from tests.conftest_types import ClientAuthenticationMethod
logger = logging.getLogger()
@pytest.mark.parametrize("client_auth_method", [
# ClientAuthenticationMethod.BASIC,
ClientAuthenticationMethod.API... | tests/integration/api/controllers/test_registries.py |
import json
import logging
import pytest
from lifemonitor.api import models
from tests import utils
from tests.conftest_types import ClientAuthenticationMethod
logger = logging.getLogger()
@pytest.mark.parametrize("client_auth_method", [
# ClientAuthenticationMethod.BASIC,
ClientAuthenticationMethod.API... | 0.549641 | 0.226848 |
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under th... | core/data_generator.py |
# 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
# https://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under th... | 0.956543 | 0.662401 |
import pymysql.cursors;
class DB_Users:
def __init__(self, connection):
self._connection = connection;
self._createTable();
def _setData(self, serverID, accountID, data, dataType):
try:
with self._connection.cursor() as cursor:
sql = "update `Users` set `{0}... | cogs/utils/DB_Users.py | import pymysql.cursors;
class DB_Users:
def __init__(self, connection):
self._connection = connection;
self._createTable();
def _setData(self, serverID, accountID, data, dataType):
try:
with self._connection.cursor() as cursor:
sql = "update `Users` set `{0}... | 0.299208 | 0.191007 |
import time
from pathlib import Path
import json
import superannotate as sa
from .test_assign_images import safe_create_project
def test_add_bbox(tmpdir):
tmpdir = Path(tmpdir)
PROJECT_NAME = "Example Project test annotation add"
PROJECT_DESCRIPTION = "test vector"
PATH_TO_SAMPLE_PROJECT = Path("./t... | tests/test_annotation_adding.py | import time
from pathlib import Path
import json
import superannotate as sa
from .test_assign_images import safe_create_project
def test_add_bbox(tmpdir):
tmpdir = Path(tmpdir)
PROJECT_NAME = "Example Project test annotation add"
PROJECT_DESCRIPTION = "test vector"
PATH_TO_SAMPLE_PROJECT = Path("./t... | 0.312685 | 0.326057 |
from records_mover.db.redshift.redshift_db_driver import RedshiftDBDriver
from records_mover.records.records_format import DelimitedRecordsFormat
import unittest
from mock import patch, MagicMock, Mock
def fake_text(s):
return (s,)
class TestRedshiftDBDriverFormatNegotiation(unittest.TestCase):
@patch('reco... | tests/unit/db/redshift/test_redshift_db_driver_format_negotiation.py | from records_mover.db.redshift.redshift_db_driver import RedshiftDBDriver
from records_mover.records.records_format import DelimitedRecordsFormat
import unittest
from mock import patch, MagicMock, Mock
def fake_text(s):
return (s,)
class TestRedshiftDBDriverFormatNegotiation(unittest.TestCase):
@patch('reco... | 0.619701 | 0.134747 |
This sample code demonstrates how to:
- load text data into python using pandas module
- preprocess textual categorical features
- build decision tree using training data
- predict scores over novel test data
"""
# Loading requisite libraries
import sys
import os
import re
import pandas as pd
import numpy as np
import... | MachineLearning/DTREE_NAIVE_BAYES/Q1/dtree_data_code/decision_tree.py | This sample code demonstrates how to:
- load text data into python using pandas module
- preprocess textual categorical features
- build decision tree using training data
- predict scores over novel test data
"""
# Loading requisite libraries
import sys
import os
import re
import pandas as pd
import numpy as np
import... | 0.583915 | 0.653597 |
from aocpuzzle import AoCPuzzle
class SignalEntry:
def __init__(self, signal_pattern: str, four_digits: str) -> None:
self.signal_pattern = signal_pattern.split()
self.four_digits = four_digits.split()
self.digits: list[set[str]] = [set()] * 10
self.build_digits()
def build_d... | src/years/2021/08/solution.py | from aocpuzzle import AoCPuzzle
class SignalEntry:
def __init__(self, signal_pattern: str, four_digits: str) -> None:
self.signal_pattern = signal_pattern.split()
self.four_digits = four_digits.split()
self.digits: list[set[str]] = [set()] * 10
self.build_digits()
def build_d... | 0.586523 | 0.529628 |
from models import base_model
from flask_pymongo import ObjectId
from werkzeug.exceptions import NotFound
from jsonmerge import merge
import datetime
class TravelModel:
def __init__(self):
self.travel = base_model.mongo.db.travel
self.user = base_model.mongo.db.user
def transform(self, travel)... | app/models/travel_model.py | from models import base_model
from flask_pymongo import ObjectId
from werkzeug.exceptions import NotFound
from jsonmerge import merge
import datetime
class TravelModel:
def __init__(self):
self.travel = base_model.mongo.db.travel
self.user = base_model.mongo.db.user
def transform(self, travel)... | 0.449393 | 0.123128 |
from abc import ABCMeta, abstractmethod
from typing import Optional
class Money(float, metaclass=ABCMeta):
"""
Handles amounts of money by subclassing float. In general it's a very bad
idea to store amounts of money as floats as all kind of funny things can
happen. But as openpyxl parses currency cell... | pydantic_xlsx/types.py | from abc import ABCMeta, abstractmethod
from typing import Optional
class Money(float, metaclass=ABCMeta):
"""
Handles amounts of money by subclassing float. In general it's a very bad
idea to store amounts of money as floats as all kind of funny things can
happen. But as openpyxl parses currency cell... | 0.935898 | 0.899431 |
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of t... | badge_generator/generate_latex.py |
# Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of t... | 0.462473 | 0.093927 |
import copy
import falcon
import json
import os
import shutil
import subprocess
import sys
import traceback
from datetime import datetime, timedelta
from sqlalchemy import func, desc
from sqlalchemy.exc import SQLAlchemyError
import model
import endpoint
import util
from db import engine, session
from util import User... | app.py | import copy
import falcon
import json
import os
import shutil
import subprocess
import sys
import traceback
from datetime import datetime, timedelta
from sqlalchemy import func, desc
from sqlalchemy.exc import SQLAlchemyError
import model
import endpoint
import util
from db import engine, session
from util import User... | 0.164081 | 0.052716 |
class Node:
def __init__(self,value):
self.info = value
self.link = None
class SingleLinked:
def __init__(self):
self.start = None
list = SingleLinked()
def insertAtBeg(value):
if list.start == None:
list.start = Node(value)
#list.start.link = None
else:
... | LinkedList/SingleLinkedListInsert.py | class Node:
def __init__(self,value):
self.info = value
self.link = None
class SingleLinked:
def __init__(self):
self.start = None
list = SingleLinked()
def insertAtBeg(value):
if list.start == None:
list.start = Node(value)
#list.start.link = None
else:
... | 0.391406 | 0.237963 |
import os
import tempfile
import time
from math import exp
import graphviz
import networkx as nx
from sppl.spe import AtomicLeaf
from sppl.spe import NominalLeaf
from sppl.spe import ProductSPE
from sppl.spe import RealLeaf
from sppl.spe import SumSPE
gensym = lambda: 'r%s' % (str(time.time()).replace('.', ''),)
... | magics/render.py |
import os
import tempfile
import time
from math import exp
import graphviz
import networkx as nx
from sppl.spe import AtomicLeaf
from sppl.spe import NominalLeaf
from sppl.spe import ProductSPE
from sppl.spe import RealLeaf
from sppl.spe import SumSPE
gensym = lambda: 'r%s' % (str(time.time()).replace('.', ''),)
... | 0.450843 | 0.286113 |
from gen.int_compute_class_gen import IntegerComputationClassGenerator
from tests.util import read_file, assert_runs_to_completion
def test_int_comp(tmp_path):
int_gen = IntegerComputationClassGenerator("IntComp", 3, 3)
int_gen.serialize(tmp_path)
actual_output = read_file(tmp_path, "IntComp.som").strip(... | tests/test_int_compute.py | from gen.int_compute_class_gen import IntegerComputationClassGenerator
from tests.util import read_file, assert_runs_to_completion
def test_int_comp(tmp_path):
int_gen = IntegerComputationClassGenerator("IntComp", 3, 3)
int_gen.serialize(tmp_path)
actual_output = read_file(tmp_path, "IntComp.som").strip(... | 0.571169 | 0.463687 |
import numpy as np
import os
import pandas as pd
import psycopg2
import time
from dotenv import (
find_dotenv,
load_dotenv
)
from pathlib import Path
from sqlalchemy import create_engine
class Backend(object):
def __init__(self, use_docker_host=True):
load_dotenv(find_dotenv())
self.pg_use... | plmdata/backend.py | import numpy as np
import os
import pandas as pd
import psycopg2
import time
from dotenv import (
find_dotenv,
load_dotenv
)
from pathlib import Path
from sqlalchemy import create_engine
class Backend(object):
def __init__(self, use_docker_host=True):
load_dotenv(find_dotenv())
self.pg_use... | 0.402744 | 0.102529 |
from django.db import migrations, models
from hexbytes import HexBytes
from gnosis.safe.safe_signature import (
SafeSignature,
SafeSignatureApprovedHash,
SafeSignatureType,
)
def add_signature_type(apps, schema_editor):
MultisigConfirmation = apps.get_model("history", "MultisigConfirmation")
Mu... | safe_transaction_service/history/migrations/0016_multisigconfirmation_signature_type.py |
from django.db import migrations, models
from hexbytes import HexBytes
from gnosis.safe.safe_signature import (
SafeSignature,
SafeSignatureApprovedHash,
SafeSignatureType,
)
def add_signature_type(apps, schema_editor):
MultisigConfirmation = apps.get_model("history", "MultisigConfirmation")
Mu... | 0.48121 | 0.19163 |
import collections
import inspect
from abc import ABC, abstractmethod
from typing import Callable, Optional, Dict, List, Any
import attrs
from databutler.datana.generic.corpus.code import DatanaFunction
from databutler.datana.generic.corpus.processing.base_processor import DatanaFunctionProcessor
from databutler.pat ... | databutler/datana/generic/corpus/processing/keyword_normalization.py | import collections
import inspect
from abc import ABC, abstractmethod
from typing import Callable, Optional, Dict, List, Any
import attrs
from databutler.datana.generic.corpus.code import DatanaFunction
from databutler.datana.generic.corpus.processing.base_processor import DatanaFunctionProcessor
from databutler.pat ... | 0.826782 | 0.280974 |
from typing import Any, Generic, List, Optional, Tuple, TypeVar, cast
import numpy as np
import torch
from torch import nn
from mlprogram.actions import ActionSequence
from mlprogram.builtins import Environment
from mlprogram.encoders import ActionSequenceEncoder
from mlprogram.languages import Parser, Token
Code = ... | mlprogram/transforms/action_sequence.py | from typing import Any, Generic, List, Optional, Tuple, TypeVar, cast
import numpy as np
import torch
from torch import nn
from mlprogram.actions import ActionSequence
from mlprogram.builtins import Environment
from mlprogram.encoders import ActionSequenceEncoder
from mlprogram.languages import Parser, Token
Code = ... | 0.922678 | 0.386648 |
import sqlite3
class Database(object):
def __init__(self):
conn = sqlite3.connect('self.db')
c = conn.cursor()
c.execute("CREATE TABLE IF NOT EXISTS self(cluster text, cloud "
"text, instance text, launch integer, benchmark text)")
conn.close()
def add(self, ... | resources/cluster/database.py | import sqlite3
class Database(object):
def __init__(self):
conn = sqlite3.connect('self.db')
c = conn.cursor()
c.execute("CREATE TABLE IF NOT EXISTS self(cluster text, cloud "
"text, instance text, launch integer, benchmark text)")
conn.close()
def add(self, ... | 0.310067 | 0.106412 |
from flask import session
from wtforms import Form, StringField, SelectField, BooleanField, SelectMultipleField
from wtforms.csrf.session import SessionCSRF
from wtforms.validators import input_required, length
from wtforms.widgets import CheckboxInput, ListWidget
def init_app(app):
BaseForm.Meta.csrf_secret = by... | odp/app/forms.py | from flask import session
from wtforms import Form, StringField, SelectField, BooleanField, SelectMultipleField
from wtforms.csrf.session import SessionCSRF
from wtforms.validators import input_required, length
from wtforms.widgets import CheckboxInput, ListWidget
def init_app(app):
BaseForm.Meta.csrf_secret = by... | 0.759136 | 0.152032 |
from datetime import datetime
import pandas as pd
import fxsignal
import fxcmpy
import yaml
import logging
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s %(message)s',
handlers=[logging.FileHandler("./logs/test_algo.log"), logging.StreamHandler()])
d... | scripts/test_algo.py | from datetime import datetime
import pandas as pd
import fxsignal
import fxcmpy
import yaml
import logging
logging.basicConfig(level=logging.INFO,
format='%(asctime)s %(levelname)-8s %(message)s',
handlers=[logging.FileHandler("./logs/test_algo.log"), logging.StreamHandler()])
d... | 0.436382 | 0.256448 |
from dataiku.runnables import Runnable
import dataiku
import urllib2, sys
import requests
import json
import os
import dl_image_toolbox_utils as utils
import pandas as pd
import constants
import time
# We deactivate GPU for this script, because all the methods only need to
# fetch information about model and do not m... | deeplearning-image-gpu/python-runnables/download-models/runnable.py | from dataiku.runnables import Runnable
import dataiku
import urllib2, sys
import requests
import json
import os
import dl_image_toolbox_utils as utils
import pandas as pd
import constants
import time
# We deactivate GPU for this script, because all the methods only need to
# fetch information about model and do not m... | 0.454472 | 0.186817 |
from nectarallocationclient import exceptions
from nectarallocationclient.v1 import allocations
from nectarallocationclient.tests.unit import utils
from nectarallocationclient.tests.unit.v1 import fakes
class AllocationsTest(utils.TestCase):
def setUp(self):
super(AllocationsTest, self).setUp()
... | nectarallocationclient/tests/unit/v1/test_allocations.py |
from nectarallocationclient import exceptions
from nectarallocationclient.v1 import allocations
from nectarallocationclient.tests.unit import utils
from nectarallocationclient.tests.unit.v1 import fakes
class AllocationsTest(utils.TestCase):
def setUp(self):
super(AllocationsTest, self).setUp()
... | 0.594316 | 0.254784 |
from pystac.extensions.item_assets import ItemAssetsExtension
from pystac.extensions.view import ViewExtension
import unittest
import pystac
from pystac.cache import CollectionCache
from pystac.serialization import (
identify_stac_object,
identify_stac_object_type,
merge_common_properties,
migrate_to_l... | tests/serialization/test_migrate.py | from pystac.extensions.item_assets import ItemAssetsExtension
from pystac.extensions.view import ViewExtension
import unittest
import pystac
from pystac.cache import CollectionCache
from pystac.serialization import (
identify_stac_object,
identify_stac_object_type,
merge_common_properties,
migrate_to_l... | 0.655557 | 0.256611 |
from os.path import abspath, dirname, join
import configparser
# All the configs are in secret.cfg that must be created in prod server when deploying
CURRENT_DIR = abspath(dirname(__file__))
configfile = configparser.ConfigParser()
configpath = abspath(join(CURRENT_DIR, "secret.cfg"))
with open(configpath, "r")... | settings/config.py | from os.path import abspath, dirname, join
import configparser
# All the configs are in secret.cfg that must be created in prod server when deploying
CURRENT_DIR = abspath(dirname(__file__))
configfile = configparser.ConfigParser()
configpath = abspath(join(CURRENT_DIR, "secret.cfg"))
with open(configpath, "r")... | 0.241489 | 0.068631 |
import numpy as np
import os
from scipy.special import softmax
import time
import shutil
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm_
from ops.dataset import TSNDataSet
from ops.models import TSN
from ops.transforms import *
from opts_cust... | demo_custom.py |
import numpy as np
import os
from scipy.special import softmax
import time
import shutil
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim
from torch.nn.utils import clip_grad_norm_
from ops.dataset import TSNDataSet
from ops.models import TSN
from ops.transforms import *
from opts_cust... | 0.543833 | 0.182863 |
# Import many helpful packages that we will use
import math, random, time, copy, multiprocessing, itertools, sys, PIL
import networkx as nx
import matplotlib.pyplot as plt
import IPython, io, os
import numpy as np
from urllib.request import urlopen
from io import BytesIO
# STEP 1.2
# Colab does not have a "video dri... | AI python Neural network/ExampleGameNN.py |
# Import many helpful packages that we will use
import math, random, time, copy, multiprocessing, itertools, sys, PIL
import networkx as nx
import matplotlib.pyplot as plt
import IPython, io, os
import numpy as np
from urllib.request import urlopen
from io import BytesIO
# STEP 1.2
# Colab does not have a "video dri... | 0.492188 | 0.466116 |
# # 113th Congress as News Commentators on Twitter
#
# In this project I am answering the following questions:
#
# * Who are the most active news commentators among senators and congressmen ?
# * Which news got the most attention by the politicians ?
# * How many news (of 7376) are commentated by democrats and/or re... | analysis/tweeps-US.py |
# # 113th Congress as News Commentators on Twitter
#
# In this project I am answering the following questions:
#
# * Who are the most active news commentators among senators and congressmen ?
# * Which news got the most attention by the politicians ?
# * How many news (of 7376) are commentated by democrats and/or re... | 0.39222 | 0.513912 |
from . import AWSObject, AWSProperty
from .validators import *
from .constants import *
# -------------------------------------------
class WAFPredicate(AWSProperty):
"""# Predicate - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/lates... | stubs/waf.py | from . import AWSObject, AWSProperty
from .validators import *
from .constants import *
# -------------------------------------------
class WAFPredicate(AWSProperty):
"""# Predicate - CloudFormationResourceSpecification version: 1.4.0
{
"Documentation": "http://docs.aws.amazon.com/AWSCloudFormation/lates... | 0.537041 | 0.132852 |
from __future__ import annotations
import dataclasses
import logging
import inspect
import pytest
import xml.etree.ElementTree as ET
from relaton_bib import FormattedString, ContributorRole, \
ContributionInfo, Organization
@pytest.fixture
def role():
return ContributorRole(type="type", description=[
... | tests/test_contribution_info.py | from __future__ import annotations
import dataclasses
import logging
import inspect
import pytest
import xml.etree.ElementTree as ET
from relaton_bib import FormattedString, ContributorRole, \
ContributionInfo, Organization
@pytest.fixture
def role():
return ContributorRole(type="type", description=[
... | 0.773943 | 0.298319 |
from dataclasses import dataclass, fields
import typing as t
from hmalib.models import Label, MatchMessage
import hmalib.common.config as config
from requests import get, post, put, delete, Response
class LabelWithConstraints(Label):
_KEY_CONSTRAINT = "KeyConstraint"
def __init__(self, value: str):
... | hasher-matcher-actioner/hmalib/common/actioner_models.py |
from dataclasses import dataclass, fields
import typing as t
from hmalib.models import Label, MatchMessage
import hmalib.common.config as config
from requests import get, post, put, delete, Response
class LabelWithConstraints(Label):
_KEY_CONSTRAINT = "KeyConstraint"
def __init__(self, value: str):
... | 0.825027 | 0.21684 |
import networkx as nx
import numpy as np
import torch
import graph as gl
import random
OFFSET_4 = torch.from_numpy(np.array([[-5, 5], [5, 5], [-5, -5], [5, -5]]).astype(np.float32))
OFFSET_9 = torch.from_numpy(np.array([[-10, -10], [0, -10], [10, -10], [-10, 0], [0, 0],
[10, 0], ... | ae_datasets/d_creator.py | import networkx as nx
import numpy as np
import torch
import graph as gl
import random
OFFSET_4 = torch.from_numpy(np.array([[-5, 5], [5, 5], [-5, -5], [5, -5]]).astype(np.float32))
OFFSET_9 = torch.from_numpy(np.array([[-10, -10], [0, -10], [10, -10], [-10, 0], [0, 0],
[10, 0], ... | 0.633297 | 0.464537 |
from module import Module, GIT_HOME
from module import ANSI_BOLD, ANSI_ERROR, ANSI_CLEAR, ANSI_SUCCESS
from tabulate import tabulate
import subprocess
CONFIGS = [
'.tmux.conf',
]
PROGRAMS = {
'tmux': 'sudo pacman -S --needed tmux',
'xsel': 'sudo pacman -S --needed xsel',
'xclip': 'sudo pacman -S --nee... | setup-arch/tmux.py | from module import Module, GIT_HOME
from module import ANSI_BOLD, ANSI_ERROR, ANSI_CLEAR, ANSI_SUCCESS
from tabulate import tabulate
import subprocess
CONFIGS = [
'.tmux.conf',
]
PROGRAMS = {
'tmux': 'sudo pacman -S --needed tmux',
'xsel': 'sudo pacman -S --needed xsel',
'xclip': 'sudo pacman -S --nee... | 0.278846 | 0.139016 |
import paho.mqtt.client as mqtt
from config import BROKER
import json
from utils import euclidean_distance
class Communication:
"""Handles MQTT communication."""
def __init__(self, teamname):
self.teamname = teamname
self.teammates = []
self.publisher = mqtt.Client(f'{teamname}_publish... | additional_code/communication_new.py | import paho.mqtt.client as mqtt
from config import BROKER
import json
from utils import euclidean_distance
class Communication:
"""Handles MQTT communication."""
def __init__(self, teamname):
self.teamname = teamname
self.teammates = []
self.publisher = mqtt.Client(f'{teamname}_publish... | 0.585338 | 0.187114 |
from __future__ import absolute_import, division, print_function, unicode_literals
from pants.backend.native.config.environment import (Assembler, CCompiler, CppCompiler,
CppToolchain, CToolchain, Linker, Platform)
from pants.backend.native.subsystems.binaries.binu... | src/python/pants/backend/native/subsystems/native_toolchain.py |
from __future__ import absolute_import, division, print_function, unicode_literals
from pants.backend.native.config.environment import (Assembler, CCompiler, CppCompiler,
CppToolchain, CToolchain, Linker, Platform)
from pants.backend.native.subsystems.binaries.binu... | 0.592902 | 0.077378 |
from ..conf_file import get_vmpooler_hostname, get_auth_token
from ..service import get_vm, list_vm, info_vm, destroy_vm, get_token_info
from ..util import pretty_print
#===================================================================================================
# Functions: Private
#===========================... | vmpooler_client/commands/vm.py | from ..conf_file import get_vmpooler_hostname, get_auth_token
from ..service import get_vm, list_vm, info_vm, destroy_vm, get_token_info
from ..util import pretty_print
#===================================================================================================
# Functions: Private
#===========================... | 0.729231 | 0.367724 |
import sys
from os import mkdir
from os.path import dirname, abspath, exists
import shutil
import time
BASE_PATH = dirname(dirname(dirname(dirname(abspath(__file__)))))
sys.path.append(BASE_PATH)
# print(BASE_PATH)
from lib import settings
from lib.command import SubprocessExecute
from lib.logger import l... | bin/logic/make/make_server.py | import sys
from os import mkdir
from os.path import dirname, abspath, exists
import shutil
import time
BASE_PATH = dirname(dirname(dirname(dirname(abspath(__file__)))))
sys.path.append(BASE_PATH)
# print(BASE_PATH)
from lib import settings
from lib.command import SubprocessExecute
from lib.logger import l... | 0.083783 | 0.03359 |
from __future__ import with_statement
import os
import os.path
import re
PKG = "django_settings_yaml"
VERSION_PY = os.path.join(PKG, "version.py")
VERSION_TEMPLATE = """# This file is generated from information obtained via git describe
# Distribution tarballs contain a pre-generated copy of this file.
__version__ =... | setup.py | from __future__ import with_statement
import os
import os.path
import re
PKG = "django_settings_yaml"
VERSION_PY = os.path.join(PKG, "version.py")
VERSION_TEMPLATE = """# This file is generated from information obtained via git describe
# Distribution tarballs contain a pre-generated copy of this file.
__version__ =... | 0.30013 | 0.078997 |
import pytest
from gwcloud_python.utils import file_filters
from gwcloud_python import FileReference, FileReferenceList
@pytest.fixture
def png_data():
return FileReferenceList([
FileReference(path='data/dir/test1.png', file_size='1', download_token='test_download_token_1'),
FileReference(path='da... | gwcloud_python/utils/tests/test_file_filters.py | import pytest
from gwcloud_python.utils import file_filters
from gwcloud_python import FileReference, FileReferenceList
@pytest.fixture
def png_data():
return FileReferenceList([
FileReference(path='data/dir/test1.png', file_size='1', download_token='test_download_token_1'),
FileReference(path='da... | 0.403214 | 0.390418 |
__author__ = 'mnowotka'
import os
import sys
import re
import hashlib
from optparse import OptionParser
import requests
import BeautifulSoup
#-------------------------------------------------------------------------------
PAGE_REGEX = re.compile(r"<<\s?<\s?(?P<current_page>\d+) of (?P<total_pages>\d+)\s?>... | sea_cli/sea.py | __author__ = 'mnowotka'
import os
import sys
import re
import hashlib
from optparse import OptionParser
import requests
import BeautifulSoup
#-------------------------------------------------------------------------------
PAGE_REGEX = re.compile(r"<<\s?<\s?(?P<current_page>\d+) of (?P<total_pages>\d+)\s?>... | 0.165931 | 0.088387 |
import pony.orm as pny
from Database import db
from Database.users import User
import datetime
import uuid
#these are all db tables related to the workflow system
class MessageLog(db.Entity):
uuid = pny.PrimaryKey(str)
status = pny.Required(str)
date_submitted = pny.Required(datetime.datetime, precision=6... | Database/workflow.py | import pony.orm as pny
from Database import db
from Database.users import User
import datetime
import uuid
#these are all db tables related to the workflow system
class MessageLog(db.Entity):
uuid = pny.PrimaryKey(str)
status = pny.Required(str)
date_submitted = pny.Required(datetime.datetime, precision=6... | 0.395601 | 0.40028 |
import csv
import pandas as pd
from collections import Counter
from nltk.tokenize import RegexpTokenizer
import time
def getFoodIdDf(description, foodIdFilePath="foodData/input_food.csv"):
colList = ["sr_description", "fdc_id"]
df = pd.read_csv(foodIdFilePath, usecols=colList)
tokenizer = RegexpTokenize... | getFoodData.py | import csv
import pandas as pd
from collections import Counter
from nltk.tokenize import RegexpTokenizer
import time
def getFoodIdDf(description, foodIdFilePath="foodData/input_food.csv"):
colList = ["sr_description", "fdc_id"]
df = pd.read_csv(foodIdFilePath, usecols=colList)
tokenizer = RegexpTokenize... | 0.289372 | 0.192672 |
from classes.strategy import Strategy
def shifted_binary_search(array, target, strategy=Strategy.Iteration):
'''
Given an array of numbers that is sorted but shifted by an unknown number
of indices in an unknown direction, find and return the first found index of
the array where array[index] == target.
If... | problems/sorting/shifted_binary_search.py | from classes.strategy import Strategy
def shifted_binary_search(array, target, strategy=Strategy.Iteration):
'''
Given an array of numbers that is sorted but shifted by an unknown number
of indices in an unknown direction, find and return the first found index of
the array where array[index] == target.
If... | 0.768646 | 0.761738 |
import matplotlib
matplotlib.use('TkAgg')
import numpy as np
import matplotlib.pyplot as plt
import sys,os
sys.path.append('/home/msdos/focalplane/plate_control/trunk/petal')
import petalcomm
import petal
import pandas as pd
import datetime
import time, datetime
import pickle
import csv
from scipy import interpolate
im... | start_petal_temp_telemetry.py | import matplotlib
matplotlib.use('TkAgg')
import numpy as np
import matplotlib.pyplot as plt
import sys,os
sys.path.append('/home/msdos/focalplane/plate_control/trunk/petal')
import petalcomm
import petal
import pandas as pd
import datetime
import time, datetime
import pickle
import csv
from scipy import interpolate
im... | 0.08363 | 0.170715 |
from argparse import ArgumentTypeError
def str_to_str_list(values):
if isinstance(values, str):
values = (str(v) for v in values.split())
else:
values = (str(v) for v in values)
return list(values)
def str_to_float_list(values):
if isinstance(values, str):
values = (float(v) ... | aevnmt/hparams/args.py | from argparse import ArgumentTypeError
def str_to_str_list(values):
if isinstance(values, str):
values = (str(v) for v in values.split())
else:
values = (str(v) for v in values)
return list(values)
def str_to_float_list(values):
if isinstance(values, str):
values = (float(v) ... | 0.803598 | 0.575588 |
import sys
import patchy_san.parameters as params
import data_processing.graphs as graphs
VERSION_TYPES = ['GLOB_OBJ_PREV', 'META_PREV', 'PROC_OBJ_PREV']
def get_graph(results):
"""
Builds a graph object from a BoltStatementResult object which is the
raw result of a neo4j query.
:param results: A Bo... | data_processing/preprocessing.py | import sys
import patchy_san.parameters as params
import data_processing.graphs as graphs
VERSION_TYPES = ['GLOB_OBJ_PREV', 'META_PREV', 'PROC_OBJ_PREV']
def get_graph(results):
"""
Builds a graph object from a BoltStatementResult object which is the
raw result of a neo4j query.
:param results: A Bo... | 0.534127 | 0.569853 |
import time
import torch
from torch import sqrt as sqrt
# [s, px, py, pz, dxy, dyz, dxz, dx2-y2, dz2, S]
def get_hop_int(input_params=torch.zeros(13)):
# input_params[0]=V_sss,
# input_params[1]=V_sps, !!!!!! --> -1
# input_params[2]=V_pps,
# input_params[3]=V_ppp,
# input_params[4]=V_sds, ... | params_pytorch_spd.py | import time
import torch
from torch import sqrt as sqrt
# [s, px, py, pz, dxy, dyz, dxz, dx2-y2, dz2, S]
def get_hop_int(input_params=torch.zeros(13)):
# input_params[0]=V_sss,
# input_params[1]=V_sps, !!!!!! --> -1
# input_params[2]=V_pps,
# input_params[3]=V_ppp,
# input_params[4]=V_sds, ... | 0.124692 | 0.373476 |
import pandas as pd
from sklearn.model_selection import train_test_split
# Code starts here
data = pd.read_csv(path)
X = data.drop(['customer.id','paid.back.loan'], axis=1)
y = data['paid.back.loan']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
# Code ends here
# -----... | Using-decision-tree-model-to-find-loan-defaulters/code.py |
import pandas as pd
from sklearn.model_selection import train_test_split
# Code starts here
data = pd.read_csv(path)
X = data.drop(['customer.id','paid.back.loan'], axis=1)
y = data['paid.back.loan']
X_train,X_test,y_train,y_test = train_test_split(X, y, test_size = 0.3, random_state = 0)
# Code ends here
# -----... | 0.448668 | 0.445107 |
import logging
import os
from oslo_config import cfg
from oslo_policy import policy
from searchlight.common import policies
LOG = logging.getLogger(__name__)
policy_file_opts = [
cfg.StrOpt("service_policy_path",
default='',
help="Base path for policy files. If not specified, "
... | searchlight-6.0.0/searchlight/service_policies.py |
import logging
import os
from oslo_config import cfg
from oslo_policy import policy
from searchlight.common import policies
LOG = logging.getLogger(__name__)
policy_file_opts = [
cfg.StrOpt("service_policy_path",
default='',
help="Base path for policy files. If not specified, "
... | 0.299003 | 0.061087 |
import matplotlib.pyplot as plt
import numpy as np
import os
import imageio
from timeit import timeit
from mpl_toolkits import mplot3d
import scipy
from fractions import Fraction
import svd as svd
from matplotlib.image import imread
from skimage import color
from skimage import io
from watermarkingliutan import *
from ... | katie/SVD_Code_OLD/watermarking/watermarkexperiments.py | import matplotlib.pyplot as plt
import numpy as np
import os
import imageio
from timeit import timeit
from mpl_toolkits import mplot3d
import scipy
from fractions import Fraction
import svd as svd
from matplotlib.image import imread
from skimage import color
from skimage import io
from watermarkingliutan import *
from ... | 0.461988 | 0.390999 |
power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate... | benchmarks/SimResults/_bigLittle_hrrs_splash_tugberk_ml/SystemIPC/EightThreads_barnes/power.py | power = {'BUSES': {'Area': 1.33155,
'Bus/Area': 1.33155,
'Bus/Gate Leakage': 0.00662954,
'Bus/Peak Dynamic': 0.0,
'Bus/Runtime Dynamic': 0.0,
'Bus/Subthreshold Leakage': 0.0691322,
'Bus/Subthreshold Leakage with power gating': 0.0259246,
'Gate... | 0.72526 | 0.302893 |
import tensorflow as tf
import numpy as np
class TextCNN(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_... | model.py | import tensorflow as tf
import numpy as np
class TextCNN(object):
"""
A CNN for text classification.
Uses an embedding layer, followed by a convolutional, max-pooling and softmax layer.
"""
def __init__(
self, sequence_length, num_classes, vocab_size,
embedding_size, filter_sizes, num_... | 0.941251 | 0.360883 |
import random
from collections import deque
import time
import torch
import numpy as np
from torchvision import transforms
def process_frame(frame):
"""
Turn game frame into small, square, grayscale image.
"""
pipeline = transforms.Compose([
transforms.ToPILImage(), # turn numpy ndarray into... | src/utils.py | import random
from collections import deque
import time
import torch
import numpy as np
from torchvision import transforms
def process_frame(frame):
"""
Turn game frame into small, square, grayscale image.
"""
pipeline = transforms.Compose([
transforms.ToPILImage(), # turn numpy ndarray into... | 0.677261 | 0.554048 |
import logging
import secrets
from convex_api import KeyPair
from .command_base import CommandBase
logger = logging.getLogger(__name__)
class AccountCreateCommand(CommandBase):
def __init__(self, sub_parser=None):
super().__init__('create', sub_parser)
def create_parser(self, sub_parser):
... | convex_api/tool/command/account_create_command.py | import logging
import secrets
from convex_api import KeyPair
from .command_base import CommandBase
logger = logging.getLogger(__name__)
class AccountCreateCommand(CommandBase):
def __init__(self, sub_parser=None):
super().__init__('create', sub_parser)
def create_parser(self, sub_parser):
... | 0.577495 | 0.091911 |
import logging
import os
import json
from logging.handlers import RotatingFileHandler
from requests_oauthlib import OAuth2Session
import datetime
import secret
import msal
import ssl
import kafka_producer
logger = logging.getLogger()
logger.setLevel('INFO')
log_path = 'log_' + os.path.basename(__file__).split('.')[0] ... | doc/api_collection_programs/azure/identity_protection_risk_detections/identity_protection_risk_detections.py | import logging
import os
import json
from logging.handlers import RotatingFileHandler
from requests_oauthlib import OAuth2Session
import datetime
import secret
import msal
import ssl
import kafka_producer
logger = logging.getLogger()
logger.setLevel('INFO')
log_path = 'log_' + os.path.basename(__file__).split('.')[0] ... | 0.509276 | 0.047184 |
from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from swagger_client.api_client import ApiClient
class RegistryApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
... | pythonclient/swagger_client/api/registry_api.py | from __future__ import absolute_import
import re # noqa: F401
# python 2 and python 3 compatibility library
import six
from swagger_client.api_client import ApiClient
class RegistryApi(object):
"""NOTE: This class is auto generated by the swagger code generator program.
Do not edit the class manually.
... | 0.712532 | 0.056809 |
import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np
import cv2
def plot3d_hsv(img, rgb_img):
h, s, v = cv2.split(img)
fig = plt.figure()
axis = fig.add_subplot(1, 1, 1, projection="3d")
pixel_colors = rgb_img.reshape((np.shape(rgb_img)[0] * np.shape(rgb_img)[1], 3))
... | utils.py |
import matplotlib.pyplot as plt
from matplotlib import colors
import numpy as np
import cv2
def plot3d_hsv(img, rgb_img):
h, s, v = cv2.split(img)
fig = plt.figure()
axis = fig.add_subplot(1, 1, 1, projection="3d")
pixel_colors = rgb_img.reshape((np.shape(rgb_img)[0] * np.shape(rgb_img)[1], 3))
... | 0.703855 | 0.487673 |
import numpy as np # linear algebra
import pandas as pd
import pickle
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
file = pd.read_excel("../datasets/SportsArticles/features.xlsx")
file.dropna()
#print(str(file.head()))
# Get columns names
#print(str(list(file)))... | training/objectivity.py | import numpy as np # linear algebra
import pandas as pd
import pickle
from sklearn.naive_bayes import MultinomialNB
from sklearn.model_selection import train_test_split
file = pd.read_excel("../datasets/SportsArticles/features.xlsx")
file.dropna()
#print(str(file.head()))
# Get columns names
#print(str(list(file)))... | 0.319652 | 0.362151 |
from smbus2 import SMBus
import time
# ADS1x15 default i2c address
I2C_address = 0x48
# General ADS1x15 family ADC class
class ADS1x15:
# ADS1x15 register address
CONVERSION_REG = 0x00
CONFIG_REG = 0x01
LO_THRESH_REG = 0x02
HI_THRESH_REG = 0x03
# Input multiplexer configuration
... | ADS1x15/ADS1x15.py | from smbus2 import SMBus
import time
# ADS1x15 default i2c address
I2C_address = 0x48
# General ADS1x15 family ADC class
class ADS1x15:
# ADS1x15 register address
CONVERSION_REG = 0x00
CONFIG_REG = 0x01
LO_THRESH_REG = 0x02
HI_THRESH_REG = 0x03
# Input multiplexer configuration
... | 0.594904 | 0.215681 |
import sys
from pathlib import Path
from typing import List, Sequence, Union
from boltons import setutils
from micropy.project.modules import ProjectModule
from micropy.stubs import StubManager
from micropy.stubs.stubs import DeviceStub
class StubsModule(ProjectModule):
"""Project module for handling Stubs.
... | micropy/project/modules/stubs.py | import sys
from pathlib import Path
from typing import List, Sequence, Union
from boltons import setutils
from micropy.project.modules import ProjectModule
from micropy.stubs import StubManager
from micropy.stubs.stubs import DeviceStub
class StubsModule(ProjectModule):
"""Project module for handling Stubs.
... | 0.75037 | 0.174551 |
from PhysicsTools.Heppy.analyzers.core.Analyzer import Analyzer
from PhysicsTools.Heppy.analyzers.core.AutoHandle import AutoHandle
from PhysicsTools.Heppy.physicsobjects.PhysicsObjects import Jet
from PhysicsTools.HeppyCore.utils.deltar import *
from PhysicsTools.HeppyCore.statistics.counter import Counter, Counters
... | PhysicsTools/Heppy/python/analyzers/objects/METAnalyzer.py | from PhysicsTools.Heppy.analyzers.core.Analyzer import Analyzer
from PhysicsTools.Heppy.analyzers.core.AutoHandle import AutoHandle
from PhysicsTools.Heppy.physicsobjects.PhysicsObjects import Jet
from PhysicsTools.HeppyCore.utils.deltar import *
from PhysicsTools.HeppyCore.statistics.counter import Counter, Counters
... | 0.331012 | 0.17037 |
import numpy as np
import os
import gzip
import urllib.request
def extract_data(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(num_images*28*28)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = (dat... | aix360/datasets/mnist_dataset.py |
import numpy as np
import os
import gzip
import urllib.request
def extract_data(filename, num_images):
with gzip.open(filename) as bytestream:
bytestream.read(16)
buf = bytestream.read(num_images*28*28)
data = np.frombuffer(buf, dtype=np.uint8).astype(np.float32)
data = (dat... | 0.211906 | 0.321567 |
import datetime
import click
from aspen.config.config import Config
from aspen.database.connection import (
get_db_uri,
init_db,
session_scope,
SqlAlchemyInterface,
)
from aspen.database.models import (
GisaidDumpWorkflow,
ProcessedGisaidDump,
RawGisaidDump,
WorkflowStatusType,
)
from ... | src/backend/aspen/workflows/transform_gisaid/save.py | import datetime
import click
from aspen.config.config import Config
from aspen.database.connection import (
get_db_uri,
init_db,
session_scope,
SqlAlchemyInterface,
)
from aspen.database.models import (
GisaidDumpWorkflow,
ProcessedGisaidDump,
RawGisaidDump,
WorkflowStatusType,
)
from ... | 0.363308 | 0.102036 |
from collections import namedtuple
def setup_puzzle(filename:str) -> tuple:
puzzle_input = read_input_file(filename)
return get_numbers(puzzle_input[0]), create_boards(puzzle_input[1:])
def read_input_file(filename: str = "input_file.txt") -> list:
with open(filename) as input_file:
raw_input = ... | 2021/day4/puzzles.py | from collections import namedtuple
def setup_puzzle(filename:str) -> tuple:
puzzle_input = read_input_file(filename)
return get_numbers(puzzle_input[0]), create_boards(puzzle_input[1:])
def read_input_file(filename: str = "input_file.txt") -> list:
with open(filename) as input_file:
raw_input = ... | 0.645679 | 0.471527 |
import argparse
import json
import uuid
import requests
import sys
from string import Formatter
from errors import UserError, RequestError
from request_utils import Requests
from environment_utils import Environments
class Cli(object):
def __init__(
self,
requests_filename='requests.json',
... | gimme.py | import argparse
import json
import uuid
import requests
import sys
from string import Formatter
from errors import UserError, RequestError
from request_utils import Requests
from environment_utils import Environments
class Cli(object):
def __init__(
self,
requests_filename='requests.json',
... | 0.245899 | 0.086787 |
import random
import time
from itertools import product
import numpy as np
import gym
from gym import logger
env = gym.make('MountainCarContinuous-v0')
POPULATION_SIZE = 10
MAX_POPULATION_SIZE = 100
MUTATION_CHANCE = 0.5
BREEDING_CHANCE = 0.5
CURRENT_AGENTS_COUNT = 0
# Constants got from continuous_mountain_car.... | obrigatorias/inteligencia-artificial/Olivetti/Projetos/Rafael_Telles/agent.py | import random
import time
from itertools import product
import numpy as np
import gym
from gym import logger
env = gym.make('MountainCarContinuous-v0')
POPULATION_SIZE = 10
MAX_POPULATION_SIZE = 100
MUTATION_CHANCE = 0.5
BREEDING_CHANCE = 0.5
CURRENT_AGENTS_COUNT = 0
# Constants got from continuous_mountain_car.... | 0.489748 | 0.311008 |
import click
from starwhale.consts import DEFAULT_DATASET_YAML_NAME, LOCAL_FUSE_JSON_NAME
from starwhale.swds.dataset import DataSet
from starwhale.swds.store import DataSetLocalStore
@click.group("dataset", help="StarWhale DataSet(swds) build/push/convert...")
def dataset_cmd() -> None:
pass
@dataset_cmd.comm... | client/starwhale/cli/dataset.py | import click
from starwhale.consts import DEFAULT_DATASET_YAML_NAME, LOCAL_FUSE_JSON_NAME
from starwhale.swds.dataset import DataSet
from starwhale.swds.store import DataSetLocalStore
@click.group("dataset", help="StarWhale DataSet(swds) build/push/convert...")
def dataset_cmd() -> None:
pass
@dataset_cmd.comm... | 0.180612 | 0.176636 |
from antlr4 import *
from io import StringIO
from typing.io import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\u00a5")
buf.write("\u05d7\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7")
buf.write("\t\7\4\b\... | src/SqlAnalyzer/parser/SqlLexer.py | from antlr4 import *
from io import StringIO
from typing.io import TextIO
import sys
def serializedATN():
with StringIO() as buf:
buf.write("\3\u608b\ua72a\u8133\ub9ed\u417c\u3be7\u7786\u5964\2\u00a5")
buf.write("\u05d7\b\1\4\2\t\2\4\3\t\3\4\4\t\4\4\5\t\5\4\6\t\6\4\7")
buf.write("\t\7\4\b\... | 0.3027 | 0.503235 |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = [
'EventHubEventSubscriptionDestinationArgs',
'EventSubscriptionFilterArgs',
'WebHookEventSubscriptionDestinationArgs',
]
@pulumi.input_type
class E... | sdk/python/pulumi_azure_nextgen/eventgrid/v20180101/_inputs.py |
import warnings
import pulumi
import pulumi.runtime
from typing import Any, Mapping, Optional, Sequence, Union
from ... import _utilities, _tables
__all__ = [
'EventHubEventSubscriptionDestinationArgs',
'EventSubscriptionFilterArgs',
'WebHookEventSubscriptionDestinationArgs',
]
@pulumi.input_type
class E... | 0.865934 | 0.079246 |
import os
import pandas as pd
import geopandas as gpd
from shapely.geometry import mapping, Point, Polygon
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('../')
from load_paths import load_box_paths
datapath, projectpath, wdir,exe_dir, git_dir = load_box_paths()
datapath = os.path.join(d... | data_processing/ems_populations.py | import os
import pandas as pd
import geopandas as gpd
from shapely.geometry import mapping, Point, Polygon
import numpy as np
import matplotlib.pyplot as plt
import sys
sys.path.append('../')
from load_paths import load_box_paths
datapath, projectpath, wdir,exe_dir, git_dir = load_box_paths()
datapath = os.path.join(d... | 0.253953 | 0.348229 |
import os
gettext = lambda s: s
DATA_DIR = os.path.dirname(os.path.dirname(__file__))
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.django... | {{cookiecutter.project_slug}}/web/{{cookiecutter.project_slug}}/settings.py | import os
gettext = lambda s: s
DATA_DIR = os.path.dirname(os.path.dirname(__file__))
# Build paths inside the project like this: os.path.join(BASE_DIR, ...)
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__)))
# Quick-start development settings - unsuitable for production
# See https://docs.django... | 0.303113 | 0.061706 |
from cog import BasePredictor, Input, Path
from typing import Iterator
import torch
import yaml
import pathlib
import os
import yaml
from util import get_single_rgb
# https://stackoverflow.com/a/6587648/1010653
import tempfile, shutil
def create_temporary_copy(src_path):
_, tf_suffix = os.path.splitext(src_path)
... | cogrun.py | from cog import BasePredictor, Input, Path
from typing import Iterator
import torch
import yaml
import pathlib
import os
import yaml
from util import get_single_rgb
# https://stackoverflow.com/a/6587648/1010653
import tempfile, shutil
def create_temporary_copy(src_path):
_, tf_suffix = os.path.splitext(src_path)
... | 0.695855 | 0.211682 |
from spikeextractors import RecordingExtractor
from spikeextractors import SortingExtractor
import json
import numpy as np
from pathlib import Path
from .mdaio import DiskReadMda, readmda, writemda32, writemda64
import os
class MdaRecordingExtractor(RecordingExtractor):
extractor_name = 'MdaRecordingExtractor'
... | spikeextractors/extractors/mdaextractors/mdaextractors.py | from spikeextractors import RecordingExtractor
from spikeextractors import SortingExtractor
import json
import numpy as np
from pathlib import Path
from .mdaio import DiskReadMda, readmda, writemda32, writemda64
import os
class MdaRecordingExtractor(RecordingExtractor):
extractor_name = 'MdaRecordingExtractor'
... | 0.526586 | 0.163079 |
import datetime
from django.test import TestCase
from django.test import Client
import responses
from .models import Post, User
class PostTestCase(TestCase):
def setUp(self) -> None:
self.client = Client(HTTP_AUTHORIZATION="Bearer test")
return super().setUp()
@responses.activate
def t... | freefeeds/tests.py | import datetime
from django.test import TestCase
from django.test import Client
import responses
from .models import Post, User
class PostTestCase(TestCase):
def setUp(self) -> None:
self.client = Client(HTTP_AUTHORIZATION="Bearer test")
return super().setUp()
@responses.activate
def t... | 0.296756 | 0.308451 |
from micolog_plugin import *
import logging
from model import *
from google.appengine.api import users
class sys_plugin(Plugin):
def __init__(self):
Plugin.__init__(self,__file__)
self.author="xuming"
self.authoruri="http://xuming.net"
self.uri="http://xuming.net"
self.description="SysPlugin."
s... | plugins/sys_plugin/sys_plugin.py | from micolog_plugin import *
import logging
from model import *
from google.appengine.api import users
class sys_plugin(Plugin):
def __init__(self):
Plugin.__init__(self,__file__)
self.author="xuming"
self.authoruri="http://xuming.net"
self.uri="http://xuming.net"
self.description="SysPlugin."
s... | 0.12768 | 0.038829 |
import requests
class CaiClient:
"""
The CAI public API
"""
def __init__(self, user_slug, bot_slug, version_slug, developer_token, bot_client_id, bot_client_secret):
"""
Args :
- user_slug (str) : the user slug of the bot owner on the CAI platform
- bot_slug (str) : the bot slug of the ... | dataset_translation/cai_client.py | import requests
class CaiClient:
"""
The CAI public API
"""
def __init__(self, user_slug, bot_slug, version_slug, developer_token, bot_client_id, bot_client_secret):
"""
Args :
- user_slug (str) : the user slug of the bot owner on the CAI platform
- bot_slug (str) : the bot slug of the ... | 0.709724 | 0.228974 |
import matplotlib.pyplot as plt
# plotting
from vivarium.plots.simulation_output import plot_variables
from vivarium.plots.agents_multigen import plot_agents_multigen
from vivarium_multibody.plots.snapshots import format_snapshot_data, plot_snapshots, plot_tags
import copy
def plot_single(
output,
var... | bioscrape_cobra/plot.py | import matplotlib.pyplot as plt
# plotting
from vivarium.plots.simulation_output import plot_variables
from vivarium.plots.agents_multigen import plot_agents_multigen
from vivarium_multibody.plots.snapshots import format_snapshot_data, plot_snapshots, plot_tags
import copy
def plot_single(
output,
var... | 0.65368 | 0.325226 |
import unittest
from app.utils import KMP
class KMPTests(unittest.TestCase):
def test_string_pattern_matching(self):
kmp = KMP()
pattern_1 = "abc"
dataset_1 = "abbabbabbcbabcaabbababcababa"
expected_1 = [11, 20]
self.assertListEqual(kmp.search(dataset_1, pattern_1), expec... | tests/utils/kmp_tests.py | import unittest
from app.utils import KMP
class KMPTests(unittest.TestCase):
def test_string_pattern_matching(self):
kmp = KMP()
pattern_1 = "abc"
dataset_1 = "abbabbabbcbabcaabbababcababa"
expected_1 = [11, 20]
self.assertListEqual(kmp.search(dataset_1, pattern_1), expec... | 0.613584 | 0.669583 |
class NeuroConfig(object):
"""Stores datasets, backends and npy arrays in memory when the
server is launched. Created once per server execution"""
_counter = 0
def __init__(self):
NeuroConfig._counter += 1
self._id = NeuroConfig._counter
self.entries = {
"j0251... | python/neuroglancer/config.py | class NeuroConfig(object):
"""Stores datasets, backends and npy arrays in memory when the
server is launched. Created once per server execution"""
_counter = 0
def __init__(self):
NeuroConfig._counter += 1
self._id = NeuroConfig._counter
self.entries = {
"j0251... | 0.817137 | 0.206114 |
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
plt.ion() ... | TransferLearing.py |
from __future__ import print_function, division
import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
import numpy as np
import torchvision
from torchvision import datasets, models, transforms
import matplotlib.pyplot as plt
import time
import os
import copy
plt.ion() ... | 0.863852 | 0.59884 |
import warnings
from typing import Union
from torch import nn as nn
from torchvision import transforms as TF
from vp_suite.utils.models import ScaleToModel, ScaleToTest
from vp_suite.base import VPModel, VPDataset
from vp_suite.utils.dataset_wrapper import VPDatasetWrapper
def check_model_and_data_compat(model: VPMo... | vp_suite/utils/compatibility.py | import warnings
from typing import Union
from torch import nn as nn
from torchvision import transforms as TF
from vp_suite.utils.models import ScaleToModel, ScaleToTest
from vp_suite.base import VPModel, VPDataset
from vp_suite.utils.dataset_wrapper import VPDatasetWrapper
def check_model_and_data_compat(model: VPMo... | 0.895984 | 0.516535 |
import os
from os.path import join
import pandas as pd
import re
import argparse
class HucDirectory(object):
def __init__(self, path, limit_branches=[]):
self.dir = path
self.name = os.path.basename(path)
self.limit_branches = limit_branches
self.usgs_dtypes = {'location_id':str... | src/usgs_gage_aggregate.py |
import os
from os.path import join
import pandas as pd
import re
import argparse
class HucDirectory(object):
def __init__(self, path, limit_branches=[]):
self.dir = path
self.name = os.path.basename(path)
self.limit_branches = limit_branches
self.usgs_dtypes = {'location_id':str... | 0.294621 | 0.159708 |
from __future__ import print_function
import os, sys, time
startTime = time.time()
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'common')))
import driver, scenario_common, utils, vcoptparse, workload_runner
class ReplicaSequence(object):
def __init__(self, string):
... | test/scenarios/more_or_less_secondaries.py |
from __future__ import print_function
import os, sys, time
startTime = time.time()
sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), os.path.pardir, 'common')))
import driver, scenario_common, utils, vcoptparse, workload_runner
class ReplicaSequence(object):
def __init__(self, string):
... | 0.419648 | 0.23001 |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def generate_data_ajive_fig2(seed=None, all_components=False):
"""
Samples the data from AJIVE figure 2. Note here we use rows as observations
i.e. data matrices are n x d where n = # observations.
Parameter... | jive/ajive_fig2.py | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
def generate_data_ajive_fig2(seed=None, all_components=False):
"""
Samples the data from AJIVE figure 2. Note here we use rows as observations
i.e. data matrices are n x d where n = # observations.
Parameter... | 0.601125 | 0.64912 |
from collections import defaultdict
import pymysql
import os
from statsmodels.stats.inter_rater import fleiss_kappa
connection = pymysql.connect(host=os.getenv("DB_HOST", "localhost"),
user=os.getenv("DB_USER", "root"),
password=os.getenv("DB_PASS", ""),
... | src/scripts/dataset/kappa.py | from collections import defaultdict
import pymysql
import os
from statsmodels.stats.inter_rater import fleiss_kappa
connection = pymysql.connect(host=os.getenv("DB_HOST", "localhost"),
user=os.getenv("DB_USER", "root"),
password=os.getenv("DB_PASS", ""),
... | 0.341363 | 0.099908 |