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 |
|---|---|---|---|---|
title = 'methoxy decomposition to H + CH2O'
description = ''
frequencyScaleFactor = 1.0
"""
This example illustrates how to manually set up an Arkane input file for a small P-dep reaction system [using only the
RRHO assumption, and without tunneling, although this can be easily implemented]. Such a calculation is desi... | arkane/data/methoxy.py | title = 'methoxy decomposition to H + CH2O'
description = ''
frequencyScaleFactor = 1.0
"""
This example illustrates how to manually set up an Arkane input file for a small P-dep reaction system [using only the
RRHO assumption, and without tunneling, although this can be easily implemented]. Such a calculation is desi... | 0.828627 | 0.529081 |
__author__ = '<NAME>'
import tweepy
import pymongo
from pymongo import MongoClient
import json
import logging
logging.basicConfig(
filename='emovix_twitter_hashtags.log',
level=logging.WARNING,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%d-%m-%y %H:%M')
# Configuration pa... | emovix_twitter_hashtags.py |
__author__ = '<NAME>'
import tweepy
import pymongo
from pymongo import MongoClient
import json
import logging
logging.basicConfig(
filename='emovix_twitter_hashtags.log',
level=logging.WARNING,
format='%(asctime)s %(name)-12s %(levelname)-8s %(message)s',
datefmt='%d-%m-%y %H:%M')
# Configuration pa... | 0.398875 | 0.154855 |
load("//ros:utils.bzl", "get_stem")
load("@bazel_skylib//lib:paths.bzl", "paths")
load("@rules_cc//cc:defs.bzl", "cc_library")
load("@rules_python//python:defs.bzl", "py_library")
RosInterfaceInfo = provider(
"Provides info for interface code generation.",
fields = [
"info",
"deps",
],
)
_... | ros/interfaces.bzl | load("//ros:utils.bzl", "get_stem")
load("@bazel_skylib//lib:paths.bzl", "paths")
load("@rules_cc//cc:defs.bzl", "cc_library")
load("@rules_python//python:defs.bzl", "py_library")
RosInterfaceInfo = provider(
"Provides info for interface code generation.",
fields = [
"info",
"deps",
],
)
_... | 0.336985 | 0.134861 |
from django.contrib.auth.models import User, Group
from rest_framework import serializers
from .models import *
class UserSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = User
fields = ['url', 'username', 'email', 'groups']
# Register Serializer
class RegisterSer... | edukasi/serializers.py | from django.contrib.auth.models import User, Group
from rest_framework import serializers
from .models import *
class UserSerializer(serializers.HyperlinkedModelSerializer):
class Meta:
model = User
fields = ['url', 'username', 'email', 'groups']
# Register Serializer
class RegisterSer... | 0.410166 | 0.120905 |
from copy import deepcopy
from functools import lru_cache
s1 = {'a', 'b', 'c'}
s2 = frozenset('abc') # Hashable as long as all elements are hashable
print(hash(s2))
s2 = {frozenset({'a', 'b'}), frozenset({1, 2, 3})}
# Copy frozenset
t1 = (1, 2, [3, 4])
t2 = tuple(t1)
print(id(t1), id(t2)) # same
l1 = [1, 2, 3]
l2... | part-3/2-sets/5-frozensets.py | from copy import deepcopy
from functools import lru_cache
s1 = {'a', 'b', 'c'}
s2 = frozenset('abc') # Hashable as long as all elements are hashable
print(hash(s2))
s2 = {frozenset({'a', 'b'}), frozenset({1, 2, 3})}
# Copy frozenset
t1 = (1, 2, [3, 4])
t2 = tuple(t1)
print(id(t1), id(t2)) # same
l1 = [1, 2, 3]
l2... | 0.605916 | 0.309128 |
# Check SEM's ability to stay in the neighborhood of the (label) truth
# when initialized at the (label) truth.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import PCA
from Network import Network
from Models import StationaryLogistic, NonstationaryLogistic, Blockmodel
from Models import al... | minitest_gibbs.py |
# Check SEM's ability to stay in the neighborhood of the (label) truth
# when initialized at the (label) truth.
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.mlab import PCA
from Network import Network
from Models import StationaryLogistic, NonstationaryLogistic, Blockmodel
from Models import al... | 0.766687 | 0.676847 |
import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Category'
db.create_table('imagestore_category', (
('id', self.gf('django.db.models.fields.AutoF... | imagestore/migrations/0001_initial.py | import datetime
from south.db import db
from south.v2 import SchemaMigration
from django.db import models
class Migration(SchemaMigration):
def forwards(self, orm):
# Adding model 'Category'
db.create_table('imagestore_category', (
('id', self.gf('django.db.models.fields.AutoF... | 0.471467 | 0.104249 |
import asynctest
import unittest.mock
import os.path
from livebridge import LiveBridge, config
class RunTests(asynctest.TestCase):
async def test_run_with_loop(self):
self.loop.run_until_complete = asynctest.CoroutineMock(return_value=True)
control_file = os.path.join(os.path.dirname(__file__), "... | tests/test_run.py | import asynctest
import unittest.mock
import os.path
from livebridge import LiveBridge, config
class RunTests(asynctest.TestCase):
async def test_run_with_loop(self):
self.loop.run_until_complete = asynctest.CoroutineMock(return_value=True)
control_file = os.path.join(os.path.dirname(__file__), "... | 0.365796 | 0.277216 |
import os
import copy
import thornpy
from . import TMPLT_ENV
from .utilities import read_TO_file, get_cdb_path, get_full_path
class DrillSolverSettings():
"""Creates an object with all data necessary to write an Adams Drill solver settings (.ssf) file.
Note
----
The static funnel is stored as a :... | adamspy/adripy/solver_settings.py | import os
import copy
import thornpy
from . import TMPLT_ENV
from .utilities import read_TO_file, get_cdb_path, get_full_path
class DrillSolverSettings():
"""Creates an object with all data necessary to write an Adams Drill solver settings (.ssf) file.
Note
----
The static funnel is stored as a :... | 0.809615 | 0.418043 |
from ...jvm.lib.compat import *
from ...jvm.lib import annotate, Optional
from ...jvm.lib import public
from ...jvm.lib import classproperty
from ... import jni
from ...jvm import JVM as _JVM
@public
class JVM(_JVM):
"""Represents the Java virtual machine"""
jvm = classproperty(lambda cls: JVM._jv... | src/jt/rubicon/java/_jvm.py |
from ...jvm.lib.compat import *
from ...jvm.lib import annotate, Optional
from ...jvm.lib import public
from ...jvm.lib import classproperty
from ... import jni
from ...jvm import JVM as _JVM
@public
class JVM(_JVM):
"""Represents the Java virtual machine"""
jvm = classproperty(lambda cls: JVM._jv... | 0.751375 | 0.107204 |
import pathlib
import sys
import numpy as np
from matplotlib import pyplot as plt
from gromacs import (
read_gromacs_file,
write_gromacs_gro_file,
)
plt.style.use('seaborn-talk')
def get_positions(frame):
"""Get positions given indices."""
xpos = np.array([i for i in frame['x']])
ypos = np.array(... | split_bilayer/translate.py | import pathlib
import sys
import numpy as np
from matplotlib import pyplot as plt
from gromacs import (
read_gromacs_file,
write_gromacs_gro_file,
)
plt.style.use('seaborn-talk')
def get_positions(frame):
"""Get positions given indices."""
xpos = np.array([i for i in frame['x']])
ypos = np.array(... | 0.501465 | 0.527134 |
import json
import os
import time
from pathlib import Path
import uuid
import paho.mqtt.publish as publish
def safe_publish(topic, msg, broker, timeout=5):
if not broker:
print("No MQTT broker configured")
else:
try:
hostname, port = broker.split(':')
return publish.si... | python/choirless_lib/choirless_lib/mqtt_status.py | import json
import os
import time
from pathlib import Path
import uuid
import paho.mqtt.publish as publish
def safe_publish(topic, msg, broker, timeout=5):
if not broker:
print("No MQTT broker configured")
else:
try:
hostname, port = broker.split(':')
return publish.si... | 0.223971 | 0.057467 |
import FWCore.ParameterSet.Config as cms
import DQM.TrackingMonitor.LogMessageMonitor_cfi
LocalRecoLogMessageMon = DQM.TrackingMonitor.LogMessageMonitor_cfi.LogMessageMon.clone()
LocalRecoLogMessageMon.pluginsMonName = cms.string ( 'LocalReco' )
LocalRecoLogMessageMon.modules = cms.vstring( 'siPixelDigis', 'si... | DQM/TrackingMonitor/python/LogMessageMonitor_cff.py | import FWCore.ParameterSet.Config as cms
import DQM.TrackingMonitor.LogMessageMonitor_cfi
LocalRecoLogMessageMon = DQM.TrackingMonitor.LogMessageMonitor_cfi.LogMessageMon.clone()
LocalRecoLogMessageMon.pluginsMonName = cms.string ( 'LocalReco' )
LocalRecoLogMessageMon.modules = cms.vstring( 'siPixelDigis', 'si... | 0.345768 | 0.193719 |
import os
import sys
import time
import numpy as np
import torch
from torch import nn
from torchvision import transforms
# (N, C, H, W)
#t = torch.randint(0, 255, size = (1, 3, 720, 1280), dtype = torch.uint8)
def set_resize_layers(p_ls):
resize_m_ls = []
for p in p_ls:
m = nn.Upsample... | mobilenet_segment/test/test_resize_torch.py | import os
import sys
import time
import numpy as np
import torch
from torch import nn
from torchvision import transforms
# (N, C, H, W)
#t = torch.randint(0, 255, size = (1, 3, 720, 1280), dtype = torch.uint8)
def set_resize_layers(p_ls):
resize_m_ls = []
for p in p_ls:
m = nn.Upsample... | 0.40439 | 0.352146 |
from collections import defaultdict
train_data = [['Yes', 'No', 'No', 'Yes', 'Some', '$$$', 'No', 'Yes', 'French', '0-10', 'Yes'],
['Yes', 'No', 'No', 'Yes', 'Full', '$', 'No', 'No', 'Thai', '30-60', 'No'],
['No', 'Yes', 'No', 'No', 'Some', '$', 'No', 'No', 'Burger', '0-10', 'Yes'],
... | ht10.py | from collections import defaultdict
train_data = [['Yes', 'No', 'No', 'Yes', 'Some', '$$$', 'No', 'Yes', 'French', '0-10', 'Yes'],
['Yes', 'No', 'No', 'Yes', 'Full', '$', 'No', 'No', 'Thai', '30-60', 'No'],
['No', 'Yes', 'No', 'No', 'Some', '$', 'No', 'No', 'Burger', '0-10', 'Yes'],
... | 0.237046 | 0.244848 |
import proto # type: ignore
from google.protobuf import field_mask_pb2 # type: ignore
from google.rpc import status_pb2 # type: ignore
from google.streetview.publish_v1.types import resources
__protobuf__ = proto.module(
package='google.streetview.publish.v1',
manifest={
'PhotoView',
'Crea... | google/streetview/publish/v1/streetview-publish-v1-py/google/streetview/publish_v1/types/rpcmessages.py | import proto # type: ignore
from google.protobuf import field_mask_pb2 # type: ignore
from google.rpc import status_pb2 # type: ignore
from google.streetview.publish_v1.types import resources
__protobuf__ = proto.module(
package='google.streetview.publish.v1',
manifest={
'PhotoView',
'Crea... | 0.723016 | 0.171269 |
import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# Code starts here
df = pd.read_csv(path)
print(df.head())
print(df.info)
df.columns
columns = ['INCOME','HOME_VAL','BLUEBOOK','OLDCLAIM','CLM_AMT']
for col in columns:
d... | Imbalance/code.py | import numpy as np
import pandas as pd
import seaborn as sns
import matplotlib.pyplot as plt
from sklearn.model_selection import train_test_split
# Code starts here
df = pd.read_csv(path)
print(df.head())
print(df.info)
df.columns
columns = ['INCOME','HOME_VAL','BLUEBOOK','OLDCLAIM','CLM_AMT']
for col in columns:
d... | 0.314471 | 0.300348 |
import numpy as np
import scipy
from sklearn.utils import sparsefuncs
def normalize_by_umi(matrix):
counts_per_bc = matrix.get_counts_per_bc()
median_counts_per_bc = max(1.0, np.median(counts_per_bc))
scaling_factors = median_counts_per_bc / counts_per_bc
# Normalize each barcode's total count by medi... | lib/python/cellranger/analysis/stats.py | import numpy as np
import scipy
from sklearn.utils import sparsefuncs
def normalize_by_umi(matrix):
counts_per_bc = matrix.get_counts_per_bc()
median_counts_per_bc = max(1.0, np.median(counts_per_bc))
scaling_factors = median_counts_per_bc / counts_per_bc
# Normalize each barcode's total count by medi... | 0.870982 | 0.735547 |
import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Layer
from utils import pnsm
class PyramidNSMLayer(Layer):
'''
'''
def __init__(self, ishape, num_of_rois, nsm_iou_threshold, nsm_score_threshold, anchor_4dtensors, **kwargs):
self.ishape = ishape
self.num_of_rois = num_of_rois
... | maskrcnn/PyramidNSMLayer.py | import tensorflow as tf
import numpy as np
from tensorflow.keras.layers import Layer
from utils import pnsm
class PyramidNSMLayer(Layer):
'''
'''
def __init__(self, ishape, num_of_rois, nsm_iou_threshold, nsm_score_threshold, anchor_4dtensors, **kwargs):
self.ishape = ishape
self.num_of_rois = num_of_rois
... | 0.633637 | 0.474144 |
from azure import *
from azure.servicemanagement import *
import errno
import getopt
import os
import shutil
import subprocess
import sys
import time
# read env_local.sh
def source_env_local():
command = ['bash', '-c', 'source env_local.sh && env']
proc = subprocess.Popen(command, stdout = subprocess.PIPE)
... | udf/bazaar/distribute/azure-client.py |
from azure import *
from azure.servicemanagement import *
import errno
import getopt
import os
import shutil
import subprocess
import sys
import time
# read env_local.sh
def source_env_local():
command = ['bash', '-c', 'source env_local.sh && env']
proc = subprocess.Popen(command, stdout = subprocess.PIPE)
... | 0.273186 | 0.05875 |
from typing import List
from aws_cdk.aws_lambda import Runtime
import jsii
from aws_cdk import core as cdk
from aws_cdk import aws_lambda_nodejs
from aws_cdk.aws_ec2 import IInstance, IVpc, SubnetSelection
from aws_cdk.aws_secretsmanager import ISecret
from aws_cdk.aws_lambda_nodejs import ICommandHooks, NodejsFunctio... | scanner/stacks/graphql_api_stack.py | from typing import List
from aws_cdk.aws_lambda import Runtime
import jsii
from aws_cdk import core as cdk
from aws_cdk import aws_lambda_nodejs
from aws_cdk.aws_ec2 import IInstance, IVpc, SubnetSelection
from aws_cdk.aws_secretsmanager import ISecret
from aws_cdk.aws_lambda_nodejs import ICommandHooks, NodejsFunctio... | 0.482917 | 0.083778 |
import numpy as np
import matplotlib.pyplot as plt
import cv2
class GenCoe:
def __init__(self, dir:str, filename:str, mode="gray"):
self.dir = dir
self.filename = filename
loc = self.dir + "\\" + self.filename
self.img = cv2.imread(loc, cv2.IMREAD_UNCHANGED)
self.height, sel... | utils/gen_coe.py | import numpy as np
import matplotlib.pyplot as plt
import cv2
class GenCoe:
def __init__(self, dir:str, filename:str, mode="gray"):
self.dir = dir
self.filename = filename
loc = self.dir + "\\" + self.filename
self.img = cv2.imread(loc, cv2.IMREAD_UNCHANGED)
self.height, sel... | 0.187839 | 0.148325 |
# Install boto before running the script
# Setup AWS keys to get details from AWS Account
import argparse
import re
import sys
import time
import boto.ec2
AMI_NAMES_TO_USER = {
'amzn' : 'ec2-user',
'ubuntu' : 'ubuntu',
'CentOS' : 'root',
'DataStax' : 'ubuntu',
'CoreOS' : 'core'
}
AMI_IDS_TO_USE... | create-sshconfig.py |
# Install boto before running the script
# Setup AWS keys to get details from AWS Account
import argparse
import re
import sys
import time
import boto.ec2
AMI_NAMES_TO_USER = {
'amzn' : 'ec2-user',
'ubuntu' : 'ubuntu',
'CentOS' : 'root',
'DataStax' : 'ubuntu',
'CoreOS' : 'core'
}
AMI_IDS_TO_USE... | 0.402979 | 0.065425 |
import pandas as pd
import pytest
from tabelio.mock import mock_table_data
from tabelio.table import (FORMATS, BaseFormat, _find_format,
convert_table_file, read_table_format,
write_table_format)
KNOWN_EXT = 'csv'
UNKNOWN_EXT = 'unknown'
@pytest.fixture
def df()... | tests/test_table.py | import pandas as pd
import pytest
from tabelio.mock import mock_table_data
from tabelio.table import (FORMATS, BaseFormat, _find_format,
convert_table_file, read_table_format,
write_table_format)
KNOWN_EXT = 'csv'
UNKNOWN_EXT = 'unknown'
@pytest.fixture
def df()... | 0.392803 | 0.379005 |
import _thread
def init(port):
import zigbee;
zigbee.init(port);
def forward():
import zigbee;
zigbee.sendString("w#");
def stop():
import zigbee;
zigbee.sendString(" #");
def backward():
import zigbee;
zigbee.sendString("s#");
def left():
import zigbee;
zigbee.sendString("a#");
def ri... | Codes/examples/functionList.py | import _thread
def init(port):
import zigbee;
zigbee.init(port);
def forward():
import zigbee;
zigbee.sendString("w#");
def stop():
import zigbee;
zigbee.sendString(" #");
def backward():
import zigbee;
zigbee.sendString("s#");
def left():
import zigbee;
zigbee.sendString("a#");
def ri... | 0.173498 | 0.041696 |
import json
import shutil
from collections import namedtuple
from ansible.parsing.dataloader import DataLoader
from ansible.vars.manager import VariableManager
from ansible.inventory.manager import InventoryManager
from ansible.playbook.play import Play
from ansible.executor.task_queue_manager import TaskQueueManager
... | python/ansible/ansible_2.9_api.py |
import json
import shutil
from collections import namedtuple
from ansible.parsing.dataloader import DataLoader
from ansible.vars.manager import VariableManager
from ansible.inventory.manager import InventoryManager
from ansible.playbook.play import Play
from ansible.executor.task_queue_manager import TaskQueueManager
... | 0.403802 | 0.171442 |
import os
import csv
import shutil
from fama.utils.const import ENDS, STATUS_GOOD
from fama.utils.utils import autovivify, run_external_program, run_external_program_ignoreerror
from fama.gene_assembler.contig import Contig
from fama.gene_assembler.gene import Gene
from fama.gene_assembler.gene_assembly import GeneAss... | lib/fama/gene_assembler/gene_assembler.py | import os
import csv
import shutil
from fama.utils.const import ENDS, STATUS_GOOD
from fama.utils.utils import autovivify, run_external_program, run_external_program_ignoreerror
from fama.gene_assembler.contig import Contig
from fama.gene_assembler.gene import Gene
from fama.gene_assembler.gene_assembly import GeneAss... | 0.539954 | 0.175467 |
import os
tf_version = float(os.environ["TF_VERSION"][:3])
tf_keras = bool(os.environ["TF_KERAS"] == "True")
tf_python = bool(os.environ["TF_PYTHON"] == "True")
if tf_version >= 2:
if tf_keras:
from keras_adamw.optimizers_v2 import AdamW, NadamW, SGDW
elif tf_python:
from keras_adamw.optimiz... | tests/import_selection.py | import os
tf_version = float(os.environ["TF_VERSION"][:3])
tf_keras = bool(os.environ["TF_KERAS"] == "True")
tf_python = bool(os.environ["TF_PYTHON"] == "True")
if tf_version >= 2:
if tf_keras:
from keras_adamw.optimizers_v2 import AdamW, NadamW, SGDW
elif tf_python:
from keras_adamw.optimiz... | 0.726717 | 0.325346 |
import numpy as np
from qa_tools.utils import *
from qa_tools.prediction import *
def qa_pes_errors(
df_qc, n_electrons, excitation_level=0, basis_set='aug-cc-pV5Z',
bond_length=None, return_energies=False, energy_type='total'):
"""Computes the error associated with predicting a system's absolute
ele... | qa_tools/analysis.py |
import numpy as np
from qa_tools.utils import *
from qa_tools.prediction import *
def qa_pes_errors(
df_qc, n_electrons, excitation_level=0, basis_set='aug-cc-pV5Z',
bond_length=None, return_energies=False, energy_type='total'):
"""Computes the error associated with predicting a system's absolute
ele... | 0.857321 | 0.607197 |
from django.conf import settings
from django_statsd.clients import statsd
from lib.geoip import GeoIP
import mkt
class RegionMiddleware(object):
"""Figure out the user's region and store it in a cookie."""
def __init__(self):
self.geoip = GeoIP(settings)
def region_from_request(self, request)... | mkt/regions/middleware.py | from django.conf import settings
from django_statsd.clients import statsd
from lib.geoip import GeoIP
import mkt
class RegionMiddleware(object):
"""Figure out the user's region and store it in a cookie."""
def __init__(self):
self.geoip = GeoIP(settings)
def region_from_request(self, request)... | 0.599837 | 0.163345 |
"""Khronos OpenGL gl.xml to C++ GL wrapper generator."""
import argparse
import json
import os
import re
import xml.etree.ElementTree as ET
from collections import defaultdict
from config import (
EXTENSION_SUFFIXES,
RESERVED_NAMES,
FUNCTION_SUFFIXES,
HANDLE_TYPES,
EXCLUDED_ENUMS,
EXTRA_ENUM_GR... | src/erhe/gl/generate_sources.py | """Khronos OpenGL gl.xml to C++ GL wrapper generator."""
import argparse
import json
import os
import re
import xml.etree.ElementTree as ET
from collections import defaultdict
from config import (
EXTENSION_SUFFIXES,
RESERVED_NAMES,
FUNCTION_SUFFIXES,
HANDLE_TYPES,
EXCLUDED_ENUMS,
EXTRA_ENUM_GR... | 0.704973 | 0.122549 |
import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.ContactModel import ContactModel
class AlipayOpenAgentCreateModel(object):
def __init__(self):
self._account = None
self._contact_info = None
self._order_ticket = None
@property
def accou... | alipay/aop/api/domain/AlipayOpenAgentCreateModel.py | import json
from alipay.aop.api.constant.ParamConstants import *
from alipay.aop.api.domain.ContactModel import ContactModel
class AlipayOpenAgentCreateModel(object):
def __init__(self):
self._account = None
self._contact_info = None
self._order_ticket = None
@property
def accou... | 0.437703 | 0.07333 |
from multiprocessing import Process, Pool
from time import sleep, time
from express.database import *
from express.settings import *
from express.logging import Log, f
from express.prices import update_pricelist
from express.config import *
from express.client import Client
from express.offer import Offer, valuate
fro... | main.py | from multiprocessing import Process, Pool
from time import sleep, time
from express.database import *
from express.settings import *
from express.logging import Log, f
from express.prices import update_pricelist
from express.config import *
from express.client import Client
from express.offer import Offer, valuate
fro... | 0.287068 | 0.161949 |
import bcrypt
from datetime import datetime
from app.database import BaseMixin, db
class User(BaseMixin, db.Model):
__tablename__ = 'users'
userID = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String, nullable=False)
_password = db.Column(db.Binary(60))
vorname = db.Column(... | server/app/api/user/models.py | import bcrypt
from datetime import datetime
from app.database import BaseMixin, db
class User(BaseMixin, db.Model):
__tablename__ = 'users'
userID = db.Column(db.Integer, primary_key=True)
username = db.Column(db.String, nullable=False)
_password = db.Column(db.Binary(60))
vorname = db.Column(... | 0.332202 | 0.083965 |
import logging
import os
from dataclasses import dataclass
from typing import Mapping, Optional, Tuple
from pants.base.build_environment import get_buildroot
from pants.base.exception_sink import ExceptionSink
from pants.base.exiter import PANTS_FAILED_EXIT_CODE, PANTS_SUCCEEDED_EXIT_CODE, ExitCode
from pants.base.sp... | src/python/pants/bin/local_pants_runner.py |
import logging
import os
from dataclasses import dataclass
from typing import Mapping, Optional, Tuple
from pants.base.build_environment import get_buildroot
from pants.base.exception_sink import ExceptionSink
from pants.base.exiter import PANTS_FAILED_EXIT_CODE, PANTS_SUCCEEDED_EXIT_CODE, ExitCode
from pants.base.sp... | 0.868172 | 0.085671 |
from akashic.arules.transpiler import Transpiler
from akashic.ads.data_provider import DataProvider
from akashic.env_provider import EnvProvider
from akashic.bridges.data_bridge import DataBridge
from akashic.bridges.time_bridge import TimeBridge
from os.path import join, dirname, abspath
import json
def test_rule_... | test/main.py | from akashic.arules.transpiler import Transpiler
from akashic.ads.data_provider import DataProvider
from akashic.env_provider import EnvProvider
from akashic.bridges.data_bridge import DataBridge
from akashic.bridges.time_bridge import TimeBridge
from os.path import join, dirname, abspath
import json
def test_rule_... | 0.41324 | 0.267214 |
from RLBench import Bench, BenchConfig
from RLBench.bench import BenchRun
from RLBench.algo import PolicyGradient
from RLBench.envs import LinearCar
from mock import Mock, MagicMock, patch
from unittest2 import TestCase
import logging
logger = logging.getLogger(__name__)
class TestBench(TestCase):
"""Bench te... | RLBench/test/test_bench.py | from RLBench import Bench, BenchConfig
from RLBench.bench import BenchRun
from RLBench.algo import PolicyGradient
from RLBench.envs import LinearCar
from mock import Mock, MagicMock, patch
from unittest2 import TestCase
import logging
logger = logging.getLogger(__name__)
class TestBench(TestCase):
"""Bench te... | 0.898514 | 0.453201 |
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def train_test_compare(train_df, test_df):
"""
Comparing the details of train and test datasets
PARAMETERS
train_df : Training set pandas dataframe (dataframe)
test_df : Testing set pandas dataframe (dataframe)
... | utils/data_background.py | import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
def train_test_compare(train_df, test_df):
"""
Comparing the details of train and test datasets
PARAMETERS
train_df : Training set pandas dataframe (dataframe)
test_df : Testing set pandas dataframe (dataframe)
... | 0.602763 | 0.700312 |
import json
from datatypes import Metrics
def loadMetrics(metricsFilename):
metrics = {}
try:
with open(metricsFilename, 'r') as f:
js = json.load(f)
# expecting dict of prj name to sub-dict
for prj_name, prj_dict in js.items():
prj_metrics = {}
... | metricsfile.py |
import json
from datatypes import Metrics
def loadMetrics(metricsFilename):
metrics = {}
try:
with open(metricsFilename, 'r') as f:
js = json.load(f)
# expecting dict of prj name to sub-dict
for prj_name, prj_dict in js.items():
prj_metrics = {}
... | 0.386069 | 0.168925 |
from sys import stdin, stdout
from copy import deepcopy
def extendAtlas(atlas):
global showAtlas
innerAtlas = deepcopy(atlas)
incrementLine = (lambda line: list(map((lambda number: number+1 if number < 9 else 1), line)))
incrementAtlas = (lambda atlas: list(map(incrementLine, atlas)))
for i in r... | 2021/day15/part2/main.py | from sys import stdin, stdout
from copy import deepcopy
def extendAtlas(atlas):
global showAtlas
innerAtlas = deepcopy(atlas)
incrementLine = (lambda line: list(map((lambda number: number+1 if number < 9 else 1), line)))
incrementAtlas = (lambda atlas: list(map(incrementLine, atlas)))
for i in r... | 0.06101 | 0.409634 |
import utilAlgorithm
from numpy import *
from logger import logger
from utilfile import *
from utilconfigration import cfg
class utilAlg_Mean(utilAlgorithm.utilAlgorithm):
def __init__(self):
print('utilAlg_Mean __init__', self.__class__.__name__)
def trainData(self, trainX, trainY, train_attri_dict,... | Algorithm/MachineLearning/TianChi/CarSellPredict/src/utilAlg_Mean.py | import utilAlgorithm
from numpy import *
from logger import logger
from utilfile import *
from utilconfigration import cfg
class utilAlg_Mean(utilAlgorithm.utilAlgorithm):
def __init__(self):
print('utilAlg_Mean __init__', self.__class__.__name__)
def trainData(self, trainX, trainY, train_attri_dict,... | 0.250179 | 0.188175 |
import tensorflow as tf
import numpy as np
class VGG19:
def __init__(self,VGG19_Model_Path = None):
self.wDict = np.load(VGG19_Model_Path, encoding="bytes").item()
def build(self,picture):
self.conv1_1 = tf.nn.conv2d(
input=picture,
filter=self... | models/vgg19_tf.py | import tensorflow as tf
import numpy as np
class VGG19:
def __init__(self,VGG19_Model_Path = None):
self.wDict = np.load(VGG19_Model_Path, encoding="bytes").item()
def build(self,picture):
self.conv1_1 = tf.nn.conv2d(
input=picture,
filter=self... | 0.549761 | 0.322673 |
import subprocess
import time
import datetime
import os
import threading
import pandas as pd
'''
shop_code = 'kkakka001'
acc = 'qtumai'
passwd = '<PASSWORD>'
ip = '192.168.0.59'
port = '554'
ch = 'stream_ch00_0'
add = 'rtsp://' + acc + ':' + passwd + '@' + ip + ':' + port + '/' + ch
save_path = './s... | B2C/video_recoding.py | import subprocess
import time
import datetime
import os
import threading
import pandas as pd
'''
shop_code = 'kkakka001'
acc = 'qtumai'
passwd = '<PASSWORD>'
ip = '192.168.0.59'
port = '554'
ch = 'stream_ch00_0'
add = 'rtsp://' + acc + ':' + passwd + '@' + ip + ':' + port + '/' + ch
save_path = './s... | 0.112808 | 0.05498 |
import random
# Call comes in
call = ''
# Good morning, Thistle Hyundai computer speaking, how can I direct your call?
print('Good morning, <NAME>, this is computer speaking.\n\nHow can I direct your call?')
call = input()
# Sales call
if call.lower() == 'sales':
print('Thanks, please hold fo... | callTest.py | import random
# Call comes in
call = ''
# Good morning, Thistle Hyundai computer speaking, how can I direct your call?
print('Good morning, <NAME>, this is computer speaking.\n\nHow can I direct your call?')
call = input()
# Sales call
if call.lower() == 'sales':
print('Thanks, please hold fo... | 0.043043 | 0.051201 |
from env.tic_tac_toe_env import TicTacToe
from agent.agent import Agent
import random
import numpy as np
from PIL import Image
class TicTacToeGameManager():
def __init__(self, strategy=None, saved_model=None):
self.game = TicTacToe()
self.agent_first_cmap = {0: 177, 1: 255, 2: 0}
self.agen... | tic_tac_toe/env/game_manager.py | from env.tic_tac_toe_env import TicTacToe
from agent.agent import Agent
import random
import numpy as np
from PIL import Image
class TicTacToeGameManager():
def __init__(self, strategy=None, saved_model=None):
self.game = TicTacToe()
self.agent_first_cmap = {0: 177, 1: 255, 2: 0}
self.agen... | 0.403567 | 0.310662 |
from app import db
from flask_login import LoginManager, UserMixin
from datetime import date, datetime
from flask_restful import Resource, Api, abort, reqparse
class User(UserMixin, db.Model):
user_id = db.Column(db.Integer, primary_key=True)
email = db.Column(db.String(100), unique=True)
password = db... | app/models.py | from app import db
from flask_login import LoginManager, UserMixin
from datetime import date, datetime
from flask_restful import Resource, Api, abort, reqparse
class User(UserMixin, db.Model):
user_id = db.Column(db.Integer, primary_key=True)
email = db.Column(db.String(100), unique=True)
password = db... | 0.448185 | 0.058723 |
# @Author: <NAME> <valle>
# @Date: 10-May-2017
# @Email: <EMAIL>
# @Last modified by: valle
# @Last modified time: 16-Mar-2018
# @License: Apache license vesion 2.0
from kivy.uix.anchorlayout import AnchorLayout
from kivy.storage.jsonstore import JsonStore
from kivy.properties import ObjectProperty, ListProperty... | tpv_for_eetop/tpv/controllers/listadopdwidget.py |
# @Author: <NAME> <valle>
# @Date: 10-May-2017
# @Email: <EMAIL>
# @Last modified by: valle
# @Last modified time: 16-Mar-2018
# @License: Apache license vesion 2.0
from kivy.uix.anchorlayout import AnchorLayout
from kivy.storage.jsonstore import JsonStore
from kivy.properties import ObjectProperty, ListProperty... | 0.189821 | 0.102619 |
import unittest
from dependency_injector import containers, providers
class TraverseProviderTests(unittest.TestCase):
def test_nested_providers(self):
class Container(containers.DeclarativeContainer):
obj_factory = providers.DelegatedFactory(
dict,
foo=provide... | tests/unit/containers/test_traversal_py3.py | import unittest
from dependency_injector import containers, providers
class TraverseProviderTests(unittest.TestCase):
def test_nested_providers(self):
class Container(containers.DeclarativeContainer):
obj_factory = providers.DelegatedFactory(
dict,
foo=provide... | 0.618204 | 0.363958 |
import torch
import torch.nn as nn
import torch.nn.functional as F
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
... | u_mvs_mvsnet/losses/modules.py | import torch
import torch.nn as nn
import torch.nn.functional as F
class SSIM(nn.Module):
"""Layer to compute the SSIM loss between a pair of images
"""
def __init__(self):
super(SSIM, self).__init__()
self.mu_x_pool = nn.AvgPool2d(3, 1)
self.mu_y_pool = nn.AvgPool2d(3, 1)
... | 0.903746 | 0.603348 |
import sys
import numpy as np
import pickle
import scipy
from scipy.spatial.distance import squareform
from scipy.stats import zscore
from scipy.cluster import hierarchy
from tqdm import tqdm
from collections import namedtuple
from idpflex.distances import (rmsd_matrix, extract_coordinates)
from idpflex.cnextend imp... | idpflex/cluster.py | import sys
import numpy as np
import pickle
import scipy
from scipy.spatial.distance import squareform
from scipy.stats import zscore
from scipy.cluster import hierarchy
from tqdm import tqdm
from collections import namedtuple
from idpflex.distances import (rmsd_matrix, extract_coordinates)
from idpflex.cnextend imp... | 0.78789 | 0.58948 |
import unittest
from tplink_wr.parse import html
class TestScriptFinder(unittest.TestCase):
def test_exist(self):
finder = html.ScriptFinder()
finder.feed("<script>var abc = true;</script>")
scripts = finder.get_scripts()
self.assertEqual(scripts, ["var abc = true;"])
def tes... | tests/parse/test_html.py | import unittest
from tplink_wr.parse import html
class TestScriptFinder(unittest.TestCase):
def test_exist(self):
finder = html.ScriptFinder()
finder.feed("<script>var abc = true;</script>")
scripts = finder.get_scripts()
self.assertEqual(scripts, ["var abc = true;"])
def tes... | 0.400632 | 0.198122 |
"""Misc utils. Currently largely for assistance testing domain models."""
import copy
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
from domain_model import DomainModel
import pytest
def... | src/misc_test_utils/misc_test_utils.py | """Misc utils. Currently largely for assistance testing domain models."""
import copy
from typing import Any
from typing import Callable
from typing import Dict
from typing import List
from typing import Optional
from typing import Tuple
from typing import Union
from domain_model import DomainModel
import pytest
def... | 0.905673 | 0.386908 |
import numpy as np
import os
from numpy import linalg as LA
import matplotlib.pyplot as plt
#datapath = '../Chair_parts'
datapath = 'data/examples'
def renderBoxes2mesh_new(boxes, boxes_type, obj_names):
results = []
for box_i in range(boxes.shape[0]):
vertices = []
faces = []
obj_name ... | render2mesh.py | import numpy as np
import os
from numpy import linalg as LA
import matplotlib.pyplot as plt
#datapath = '../Chair_parts'
datapath = 'data/examples'
def renderBoxes2mesh_new(boxes, boxes_type, obj_names):
results = []
for box_i in range(boxes.shape[0]):
vertices = []
faces = []
obj_name ... | 0.119871 | 0.29922 |
__all__ = ['mahalanobis_pca_outliers']
import numpy as np
def mahalanobis_pca_outliers(X, n_components=2, threshold=2, plot=False):
"""
Compute PCA on X, then compute the malanobis distance
of all data points from the PCA components.
Params
------
X: data
n_components: int (default=2)
... | python_data_utils/sklearn/data/utils.py | __all__ = ['mahalanobis_pca_outliers']
import numpy as np
def mahalanobis_pca_outliers(X, n_components=2, threshold=2, plot=False):
"""
Compute PCA on X, then compute the malanobis distance
of all data points from the PCA components.
Params
------
X: data
n_components: int (default=2)
... | 0.90652 | 0.742235 |
from mininet.net import Mininet
from mininet.node import Controller, RemoteController, OVSController
from mininet.node import CPULimitedHost, Host, Node
from mininet.node import OVSKernelSwitch, UserSwitch
from mininet.node import IVSSwitch
from mininet.cli import CLI
from mininet.log import setLogLevel, info
from min... | Chapter10/10_7_sdn_miniedit.py |
from mininet.net import Mininet
from mininet.node import Controller, RemoteController, OVSController
from mininet.node import CPULimitedHost, Host, Node
from mininet.node import OVSKernelSwitch, UserSwitch
from mininet.node import IVSSwitch
from mininet.cli import CLI
from mininet.log import setLogLevel, info
from min... | 0.644001 | 0.061312 |
import os
import unittest
import typing
import math
import collections
def get_file_contents() -> str:
dir_path = os.path.dirname(os.path.realpath(__file__))
file_path = os.path.join(dir_path, "..", "data", "d14.txt")
with open(file_path, "r") as f:
lines = f.read()
return lines
ChemicalName... | python/p14.py | import os
import unittest
import typing
import math
import collections
def get_file_contents() -> str:
dir_path = os.path.dirname(os.path.realpath(__file__))
file_path = os.path.join(dir_path, "..", "data", "d14.txt")
with open(file_path, "r") as f:
lines = f.read()
return lines
ChemicalName... | 0.409221 | 0.373333 |
from lib.cuckoo.common.abstracts import Signature
class RansomwareExtensions(Signature):
name = "ransomware_extensions"
description = "Appends known ransomware file extensions to files that have been encrypted"
severity = 3
categories = ["ransomware"]
authors = ["<NAME>"]
indicators = [
... | modules/signatures/windows/ransomware_fileextensions.py |
from lib.cuckoo.common.abstracts import Signature
class RansomwareExtensions(Signature):
name = "ransomware_extensions"
description = "Appends known ransomware file extensions to files that have been encrypted"
severity = 3
categories = ["ransomware"]
authors = ["<NAME>"]
indicators = [
... | 0.510252 | 0.224459 |
from plugin.core.constants import PLUGIN_VERSION_BASE
from plugin.core.helpers.variable import all
from lxml import etree
import shutil
import os
class FSMigrator(object):
migrations = []
@classmethod
def register(cls, migration):
cls.migrations.append(migration())
@classmethod
def run(... | Trakttv.bundle/Contents/Code/fs_migrator.py | from plugin.core.constants import PLUGIN_VERSION_BASE
from plugin.core.helpers.variable import all
from lxml import etree
import shutil
import os
class FSMigrator(object):
migrations = []
@classmethod
def register(cls, migration):
cls.migrations.append(migration())
@classmethod
def run(... | 0.500488 | 0.099996 |
from itertools import chain
from util import nub
import numpy as np
import string
from collections import OrderedDict
UNK_TOKEN = "*UNK*"
START_TOKEN = "*START*"
END_TOKEN = "*END*"
PRINTABLE = set(string.printable)
def main():
validation_data_file, validation_label_file, train_data_file, train_label_file = "./so... | my_soft_pattern.py | from itertools import chain
from util import nub
import numpy as np
import string
from collections import OrderedDict
UNK_TOKEN = "*UNK*"
START_TOKEN = "*START*"
END_TOKEN = "*END*"
PRINTABLE = set(string.printable)
def main():
validation_data_file, validation_label_file, train_data_file, train_label_file = "./so... | 0.327346 | 0.244775 |
from pyradur import Dict
from pyradur.db import Sqlite3DB
from pyradur.server import SockServer
import tempfile
import threading
import unittest
import shutil
import os
import logging
import sys
class CommonTests(object):
use_cache = True
close_on_cleanup = True
def _server_thread(self, event):
t... | pyradur/tests/test_pyradur.py |
from pyradur import Dict
from pyradur.db import Sqlite3DB
from pyradur.server import SockServer
import tempfile
import threading
import unittest
import shutil
import os
import logging
import sys
class CommonTests(object):
use_cache = True
close_on_cleanup = True
def _server_thread(self, event):
t... | 0.412648 | 0.152789 |
from linked_list import SinglyLinkedList, SinglyLinkedNode
def inner_step(n1, n2, n3, sum_ll, carry):
total = carry
if n1:
total += n1.value
n1 = n1.next
if n2:
total += n2.value
n2 = n2.next
result = total % 10
carry = total // 10
new_node = SinglyLinkedNode(res... | ch02_linked_lists/q05_sum_lists.py | from linked_list import SinglyLinkedList, SinglyLinkedNode
def inner_step(n1, n2, n3, sum_ll, carry):
total = carry
if n1:
total += n1.value
n1 = n1.next
if n2:
total += n2.value
n2 = n2.next
result = total % 10
carry = total // 10
new_node = SinglyLinkedNode(res... | 0.328314 | 0.369002 |
import os
import json
from typing import Dict, List, Optional, Union, cast
import requests
from requests import get
import bs4
from bs4 import BeautifulSoup
import pandas as pd
from env import github_token, github_username
#----------------------------------------------------------------------------------------------... | acquire.py | import os
import json
from typing import Dict, List, Optional, Union, cast
import requests
from requests import get
import bs4
from bs4 import BeautifulSoup
import pandas as pd
from env import github_token, github_username
#----------------------------------------------------------------------------------------------... | 0.278061 | 0.239427 |
print('Start next file, \'page_04\'')
# imports
from openpyxl import load_workbook
from openpyxl.styles import Alignment, Border, Side, NamedStyle, Font, PatternFill
wb = load_workbook(filename = 'Plymouth_Daily_Rounds.xlsx')
sheet = wb["Page_04"]
print('Active sheet is ', sheet)
print('04-01')
wb.save('Plymouth_Daily_... | archive/page_04_firepprm_docking - Copy.py | print('Start next file, \'page_04\'')
# imports
from openpyxl import load_workbook
from openpyxl.styles import Alignment, Border, Side, NamedStyle, Font, PatternFill
wb = load_workbook(filename = 'Plymouth_Daily_Rounds.xlsx')
sheet = wb["Page_04"]
print('Active sheet is ', sheet)
print('04-01')
wb.save('Plymouth_Daily_... | 0.261897 | 0.259204 |
import random
import string
from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.utils.text import slugify
from .utils import upload_track_to, upload_image_to
class Genre(models.Model):
... | edmproducers/models.py | import random
import string
from django.db import models
from django.contrib.auth.models import User
from django.db.models.signals import post_save
from django.dispatch import receiver
from django.utils.text import slugify
from .utils import upload_track_to, upload_image_to
class Genre(models.Model):
... | 0.516595 | 0.065515 |
import getopt
import os
import subprocess
import sys
import toml
# Set the path to the configuration file
CONFIG_PATH = ""
def decrypt(config, key):
# Call gocryptfs process
path_cipher = config[key]["cipher"]
path_plain = config[key]["plain"]
subprocess.run(["gocryptfs", path_cipher, path_plain])
d... | main.py | import getopt
import os
import subprocess
import sys
import toml
# Set the path to the configuration file
CONFIG_PATH = ""
def decrypt(config, key):
# Call gocryptfs process
path_cipher = config[key]["cipher"]
path_plain = config[key]["plain"]
subprocess.run(["gocryptfs", path_cipher, path_plain])
d... | 0.137532 | 0.103612 |
description = 'Vacuum gauges in the neutron guide'
devices = dict(
vac1 = device('nicos.devices.generic.VirtualMotor',
description = 'Vacuum sensor 1 in neutron guide',
abslimits = (0, 1000),
pollinterval = 10,
maxage = 12,
unit = 'mbar',
curvalue = 1.1e-4,
f... | nicos_ess/cspec/setups/vacuum.py | description = 'Vacuum gauges in the neutron guide'
devices = dict(
vac1 = device('nicos.devices.generic.VirtualMotor',
description = 'Vacuum sensor 1 in neutron guide',
abslimits = (0, 1000),
pollinterval = 10,
maxage = 12,
unit = 'mbar',
curvalue = 1.1e-4,
f... | 0.646906 | 0.520984 |
import numpy as np
from scipy import ndimage
from time import clock
from pygeonet_rasterio import *
from pygeonet_vectorio import *
from pygeonet_plot import *
def Channel_Head_Definition(skeletonFromFlowAndCurvatureArray, geodesicDistanceArray):
# Locating end points
print 'Locating skeleton end po... | pygeonet_channel_head_definition.py | import numpy as np
from scipy import ndimage
from time import clock
from pygeonet_rasterio import *
from pygeonet_vectorio import *
from pygeonet_plot import *
def Channel_Head_Definition(skeletonFromFlowAndCurvatureArray, geodesicDistanceArray):
# Locating end points
print 'Locating skeleton end po... | 0.479504 | 0.521167 |
import json
from itertools import combinations
from math import log
import scipy.interpolate
from pymatgen.entries.computed_entries import ComputedEntry
from s4.data import open_data
__author__ = '<NAME>'
__email__ = '<EMAIL>'
__maintainer__ = '<NAME>'
__all__ = [
'finite_dg_correction',
]
with open_data('Elem... | s4/thermo/calc/finite_g.py | import json
from itertools import combinations
from math import log
import scipy.interpolate
from pymatgen.entries.computed_entries import ComputedEntry
from s4.data import open_data
__author__ = '<NAME>'
__email__ = '<EMAIL>'
__maintainer__ = '<NAME>'
__all__ = [
'finite_dg_correction',
]
with open_data('Elem... | 0.744471 | 0.263671 |
import unicodedata
combining = set()
col_widths = [7, 54, 20]
rows = [['MacRom', 'UTF-8 NFC', 'UTF-8 NFD']]
for i in range(256):
rows.append(['[%02X]' % i])
for form in ('NFC', 'NFD'):
unistr = bytes([i]).decode('mac_roman')
unistr = unicodedata.normalize(form, unistr)
codepoints = []... | MacRomanExploration.py |
import unicodedata
combining = set()
col_widths = [7, 54, 20]
rows = [['MacRom', 'UTF-8 NFC', 'UTF-8 NFD']]
for i in range(256):
rows.append(['[%02X]' % i])
for form in ('NFC', 'NFD'):
unistr = bytes([i]).decode('mac_roman')
unistr = unicodedata.normalize(form, unistr)
codepoints = []... | 0.094278 | 0.503113 |
import math
import time
t1 = time.time()
size = 2000
sizet = size*size
s = [0]*sizet
for k in range(1,56):
s[k-1] = (100003-200003*k+300007*k*k*k)%1000000-500000
for k in range(56,4000001):
s[k-1] = (s[k-1-24]+s[k-1-55]+1000000)%1000000-500000
#print(s[10-1],s[100-1])
'''
# test case
s = [-2,5,3,2,9,-... | Problem 001-150 Python/pb149.py | import math
import time
t1 = time.time()
size = 2000
sizet = size*size
s = [0]*sizet
for k in range(1,56):
s[k-1] = (100003-200003*k+300007*k*k*k)%1000000-500000
for k in range(56,4000001):
s[k-1] = (s[k-1-24]+s[k-1-55]+1000000)%1000000-500000
#print(s[10-1],s[100-1])
'''
# test case
s = [-2,5,3,2,9,-... | 0.07107 | 0.239161 |
from datetime import datetime
from flask import request
from flask_restx import Resource
import json
from io import StringIO
import boto3
import pandas as pd
import numpy as np
from .security import require_auth
from . import api_rest
class SecureResource(Resource):
""" Calls require_auth decorator on all reque... | app/api/resources.py | from datetime import datetime
from flask import request
from flask_restx import Resource
import json
from io import StringIO
import boto3
import pandas as pd
import numpy as np
from .security import require_auth
from . import api_rest
class SecureResource(Resource):
""" Calls require_auth decorator on all reque... | 0.460046 | 0.144209 |
"""Create a new CA pool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from googlecloudsdk.api_lib.privateca import base as privateca_base
from googlecloudsdk.api_lib.privateca import request_utils
from googlecloudsdk.calliope import base
from google... | lib/surface/privateca/pools/create.py | """Create a new CA pool."""
from __future__ import absolute_import
from __future__ import division
from __future__ import unicode_literals
from googlecloudsdk.api_lib.privateca import base as privateca_base
from googlecloudsdk.api_lib.privateca import request_utils
from googlecloudsdk.calliope import base
from google... | 0.727104 | 0.120983 |
from django.db import models
from django.utils.translation import ugettext_lazy as _
from ...core.models import TimeStampedModel
from ...core.utils.slug import slugify_uniquely_for_queryset
from ..choices import RANK_OPTIONS
from ..mixins import DueDateMixin
from .. import models as proj_models
class IssueStatus(m... | project_dashboard/projects/models/issue.py |
from django.db import models
from django.utils.translation import ugettext_lazy as _
from ...core.models import TimeStampedModel
from ...core.utils.slug import slugify_uniquely_for_queryset
from ..choices import RANK_OPTIONS
from ..mixins import DueDateMixin
from .. import models as proj_models
class IssueStatus(m... | 0.567337 | 0.086671 |
import time
import os
import mido
from mido import Message, MidiFile, MidiTrack, tempo2bpm
from pynput import keyboard
key_dict = {
# c4
"a4+":22, "b4-":22, "b4": 23,
# c3
"c3": 24, "c3+": 25, "d3-": 25, "d3": 26, "d3+": 27, "e3-": 27, "e3": 28,
"f3": 29, "f3+": 30, "g3-": 30, ... | vimusic.py |
import time
import os
import mido
from mido import Message, MidiFile, MidiTrack, tempo2bpm
from pynput import keyboard
key_dict = {
# c4
"a4+":22, "b4-":22, "b4": 23,
# c3
"c3": 24, "c3+": 25, "d3-": 25, "d3": 26, "d3+": 27, "e3-": 27, "e3": 28,
"f3": 29, "f3+": 30, "g3-": 30, ... | 0.406862 | 0.403861 |
from numpy.core.arrayprint import BoolFormat
from game import *
from encoder import *
from arena import *
from dataManager import *
from network import *
class Program:
def __init__(self,the_game):
self.the_game = the_game
self.best_network = readNeuralNetwork("networks/best_network")
self... | program_test.py | from numpy.core.arrayprint import BoolFormat
from game import *
from encoder import *
from arena import *
from dataManager import *
from network import *
class Program:
def __init__(self,the_game):
self.the_game = the_game
self.best_network = readNeuralNetwork("networks/best_network")
self... | 0.446253 | 0.278994 |
import flask
import glob
import json
import os
import pandas as pd
import sys
import webbrowser
from datetime import datetime
from flask import Flask, request
from flask_cors import CORS
app = Flask(__name__, static_url_path='')
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0 # TODO remove in prod
CORS(app)
project_dir ... | tools/server.py | import flask
import glob
import json
import os
import pandas as pd
import sys
import webbrowser
from datetime import datetime
from flask import Flask, request
from flask_cors import CORS
app = Flask(__name__, static_url_path='')
app.config['SEND_FILE_MAX_AGE_DEFAULT'] = 0 # TODO remove in prod
CORS(app)
project_dir ... | 0.186391 | 0.109634 |
import logging
import os
import re
import pandas as pd
import gamechangerml.src.text_classif.utils.entity_mentions as em
from gamechangerml.src.text_classif.utils.predict_glob import predict_glob
from gamechangerml.src.text_classif.utils.top_k_entities import top_k_entities
logger = logging.getLogger(__name__)
cla... | gamechangerml/src/text_classif/utils/entity_link.py | import logging
import os
import re
import pandas as pd
import gamechangerml.src.text_classif.utils.entity_mentions as em
from gamechangerml.src.text_classif.utils.predict_glob import predict_glob
from gamechangerml.src.text_classif.utils.top_k_entities import top_k_entities
logger = logging.getLogger(__name__)
cla... | 0.66072 | 0.150778 |
import urllib
import urllib2
import requests
import threading
import json
from time import sleep
url = 'http://localhost:8545/'
import os.path
def get_result(json_content):
content = json.loads(json_content)
try:
return content["result"]
except Exception as e:
print e
print json_con... | contract_data/contracts_collector.py | import urllib
import urllib2
import requests
import threading
import json
from time import sleep
url = 'http://localhost:8545/'
import os.path
def get_result(json_content):
content = json.loads(json_content)
try:
return content["result"]
except Exception as e:
print e
print json_con... | 0.080936 | 0.089097 |
from typing import Optional
from typing import Tuple
from typing import Union
import numpy as np
import pandas as pd
from pyspark import sql
from pyspark.sql import functions
from cape_privacy.spark import dtypes
from cape_privacy.spark.transformations import base
from cape_privacy.utils import typecheck
_FREQUENCY_... | cape_privacy/spark/transformations/perturbation.py | from typing import Optional
from typing import Tuple
from typing import Union
import numpy as np
import pandas as pd
from pyspark import sql
from pyspark.sql import functions
from cape_privacy.spark import dtypes
from cape_privacy.spark.transformations import base
from cape_privacy.utils import typecheck
_FREQUENCY_... | 0.897741 | 0.624637 |
import random
regs = ['ra', 'rb', 'rc', 'rd', 're']
def generate_imm():
return hex(random.randint(0, 0xffffffffffffffff))
def generate_mpc():
if random.randint(0, 1) == 0:
return 'mpc {}'.format(random.choice(regs))
else:
return 'mpc {} #{}'.format(random.choice(regs), generate_imm())
de... | b01lers-ctf-2020/300_railed/src/generate_random_instructions.py | import random
regs = ['ra', 'rb', 'rc', 'rd', 're']
def generate_imm():
return hex(random.randint(0, 0xffffffffffffffff))
def generate_mpc():
if random.randint(0, 1) == 0:
return 'mpc {}'.format(random.choice(regs))
else:
return 'mpc {} #{}'.format(random.choice(regs), generate_imm())
de... | 0.115025 | 0.200969 |
import torch
import torchvision
import torchvision.transforms as transforms
class BinaryDataset(torch.utils.data.Dataset):
def __init__(self, root, transform=None, return_idx=False):
x, y = torch.load(root)
self.data = x
self.labels = y
self.root = root
self.transform = tra... | dataset.py | import torch
import torchvision
import torchvision.transforms as transforms
class BinaryDataset(torch.utils.data.Dataset):
def __init__(self, root, transform=None, return_idx=False):
x, y = torch.load(root)
self.data = x
self.labels = y
self.root = root
self.transform = tra... | 0.912801 | 0.675737 |
import json
__author__ = '<NAME>'
class SiteInfo:
def __init__(self,dataname='SiteData',sitedatafile=[]):
"""
__init__: initialization
"""
self.sitedatafile = sitedatafile
self.nCase = 0
self.nameCase = []
self.SiteCase = {}
self... | pyhca/SiteSpecificInformation.py |
import json
__author__ = '<NAME>'
class SiteInfo:
def __init__(self,dataname='SiteData',sitedatafile=[]):
"""
__init__: initialization
"""
self.sitedatafile = sitedatafile
self.nCase = 0
self.nameCase = []
self.SiteCase = {}
self... | 0.089318 | 0.166404 |
from mvnc import mvncapi as mvnc
import NeuralNetwork
import cv2
import argparse
import time
import threading
#Argument parser
arg = argparse.ArgumentParser()
arg.add_argument("-m", "--mode", required=True, type=str, default="image", help="Mode of Neural Network, options: image, video")
arg.add_argument("-n", "--num",... | run.py | from mvnc import mvncapi as mvnc
import NeuralNetwork
import cv2
import argparse
import time
import threading
#Argument parser
arg = argparse.ArgumentParser()
arg.add_argument("-m", "--mode", required=True, type=str, default="image", help="Mode of Neural Network, options: image, video")
arg.add_argument("-n", "--num",... | 0.143023 | 0.138695 |
import os
import re
from .ply import lex, yacc
from collections import OrderedDict
import sublime
class Parser:
"""
Base class for a lexer/parser that has the rules defined as methods
"""
tokens = ()
precedence = ()
def __init__(self, **kw):
self.debug = kw.get('debug', 0)
sel... | proto_formatter.py |
import os
import re
from .ply import lex, yacc
from collections import OrderedDict
import sublime
class Parser:
"""
Base class for a lexer/parser that has the rules defined as methods
"""
tokens = ()
precedence = ()
def __init__(self, **kw):
self.debug = kw.get('debug', 0)
sel... | 0.430267 | 0.172677 |
import os
import re
import logging
from unidecode import unidecode
from onecodex.exceptions import OneCodexException, UploadException
R1_FILENAME_RE = re.compile(".*[._][Rr]?[1][_.].*")
R2_FILENAME_RE = re.compile(".*[._][Rr]?[2][_.].*")
log = logging.getLogger("onecodex")
def _check_for_ascii_filename(filename, co... | onecodex/lib/files.py | import os
import re
import logging
from unidecode import unidecode
from onecodex.exceptions import OneCodexException, UploadException
R1_FILENAME_RE = re.compile(".*[._][Rr]?[1][_.].*")
R2_FILENAME_RE = re.compile(".*[._][Rr]?[2][_.].*")
log = logging.getLogger("onecodex")
def _check_for_ascii_filename(filename, co... | 0.4206 | 0.176601 |
import time
import board
import neopixel
import threading
from datetime import datetime
from gpiozero import Button
from signal import pause
#Setup the pin
#GPIO.setmode(GPIO.BOARD)
buttonPin = 16 # board.D23
button = Button(23)
# Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18
# NeoP... | main.py | import time
import board
import neopixel
import threading
from datetime import datetime
from gpiozero import Button
from signal import pause
#Setup the pin
#GPIO.setmode(GPIO.BOARD)
buttonPin = 16 # board.D23
button = Button(23)
# Choose an open pin connected to the Data In of the NeoPixel strip, i.e. board.D18
# NeoP... | 0.487795 | 0.418697 |
import os
import sqlite3
import pandas as pd
import pymongo
from dotenv import load_dotenv
'''
"How was working with MongoDB different from working with PostgreSQL?
What was easier, and what was harder?"
I would say that my biggest hurdle was simply figuring out how to get
data into each system, once I was past that... | module3-nosql-and-document-oriented-databases/rpg_nosql.py | import os
import sqlite3
import pandas as pd
import pymongo
from dotenv import load_dotenv
'''
"How was working with MongoDB different from working with PostgreSQL?
What was easier, and what was harder?"
I would say that my biggest hurdle was simply figuring out how to get
data into each system, once I was past that... | 0.096153 | 0.254871 |
import abc
import tensorflow as tf
from tensor_annotations import tensorflow as ttf
from tensor_annotations import axes
from src.channelcoding.dataclasses import FixedPermuteInterleaverSettings, RandomPermuteInterleaverSettings
from .codes import Code
from .types import Batch, Time, Channels
class Interleaver(Code... | turbo-codes/src/channelcoding/interleavers.py | import abc
import tensorflow as tf
from tensor_annotations import tensorflow as ttf
from tensor_annotations import axes
from src.channelcoding.dataclasses import FixedPermuteInterleaverSettings, RandomPermuteInterleaverSettings
from .codes import Code
from .types import Batch, Time, Channels
class Interleaver(Code... | 0.832271 | 0.249082 |
import getopt
import os
from os import path
import sys
import acg
INDENT = ' '
def declare_namespaces(namespaces, source):
return '\n'.join(['namespace %s {' % i for i in namespaces]) + '\n' + source + '\n' +'\n'.join(['}'] * len(namespaces))
def output_tofile(content, filename, outputdir):
if outputd... | tools/python/gen_string_table.py |
import getopt
import os
from os import path
import sys
import acg
INDENT = ' '
def declare_namespaces(namespaces, source):
return '\n'.join(['namespace %s {' % i for i in namespaces]) + '\n' + source + '\n' +'\n'.join(['}'] * len(namespaces))
def output_tofile(content, filename, outputdir):
if outputd... | 0.210442 | 0.096323 |
import numpy as np
import pandas as pd
from multiprocessing import Pool
from scipy.special import expit
from scipy.stats import beta
from scipy.stats import powerlaw
from opaque.betabinomial_regression import BetaBinomialRegressor
from opaque.stats import equal_tailed_interval, KL_beta
class EndtoEndSimulator:
de... | opaque/simulations/end_to_end.py | import numpy as np
import pandas as pd
from multiprocessing import Pool
from scipy.special import expit
from scipy.stats import beta
from scipy.stats import powerlaw
from opaque.betabinomial_regression import BetaBinomialRegressor
from opaque.stats import equal_tailed_interval, KL_beta
class EndtoEndSimulator:
de... | 0.620507 | 0.40536 |
import json
import string
import sys
from geopy.geocoders import Nominatim
#Open a file with tweets and get the coordinates, if it's not null
geolocator = Nominatim()
file_list = ['stream_Alice.json', 'stream_Clank_2105.json', 'stream_deadpool0803.json', 'stream_deadpool1103.json', 'stream_deadpool.json', 'stream_De... | infoprocessing.py | import json
import string
import sys
from geopy.geocoders import Nominatim
#Open a file with tweets and get the coordinates, if it's not null
geolocator = Nominatim()
file_list = ['stream_Alice.json', 'stream_Clank_2105.json', 'stream_deadpool0803.json', 'stream_deadpool1103.json', 'stream_deadpool.json', 'stream_De... | 0.077997 | 0.179297 |
from PIL import Image, ImageOps
from pathlib import Path
import os
import json
import re
MISC_IDS = {
(220, 255, 166, 255): 200, #Invisible Wall (Boundary)
(128, 128, 128, 255): 206, #Surface 0
(100, 100, 100, 255): 206, #Surface 0
(204, 186, 143, 255): 206, #Surface 0
(204, 176, 143, ... | Starbound Dungeon Converter v2/SDVv2.py | from PIL import Image, ImageOps
from pathlib import Path
import os
import json
import re
MISC_IDS = {
(220, 255, 166, 255): 200, #Invisible Wall (Boundary)
(128, 128, 128, 255): 206, #Surface 0
(100, 100, 100, 255): 206, #Surface 0
(204, 186, 143, 255): 206, #Surface 0
(204, 176, 143, ... | 0.375936 | 0.117446 |
__author__ = 'carlos.diaz'
# Autoencoder for the context data based on residual networks
import numpy as np
import matplotlib.pyplot as plt
from torch import nn
import torch
import time
import os
import nde_utils
import nde_ae
from tqdm import tqdm
import sys
mdouble = False
if mdouble is True:
print('[INFO] Us... | nlte/AEcontext.py | __author__ = 'carlos.diaz'
# Autoencoder for the context data based on residual networks
import numpy as np
import matplotlib.pyplot as plt
from torch import nn
import torch
import time
import os
import nde_utils
import nde_ae
from tqdm import tqdm
import sys
mdouble = False
if mdouble is True:
print('[INFO] Us... | 0.383526 | 0.294836 |
from oci_cli import cli_util
from oci_cli.cli_util import option
from oci_cli.aliasing import CommandGroupWithAlias
from services.dns.src.oci_cli_dns.generated import dns_cli
from oci_cli import json_skeleton_utils
import click
@dns_cli.dns_root_group.command('record', cls=CommandGroupWithAlias, help="""A DNS recor... | services/dns/src/oci_cli_dns/dns_cli_extended.py |
from oci_cli import cli_util
from oci_cli.cli_util import option
from oci_cli.aliasing import CommandGroupWithAlias
from services.dns.src.oci_cli_dns.generated import dns_cli
from oci_cli import json_skeleton_utils
import click
@dns_cli.dns_root_group.command('record', cls=CommandGroupWithAlias, help="""A DNS recor... | 0.549641 | 0.096238 |
from blackjack.card import Card
from blackjack.deck import Deck
from blackjack.player import Player
class _Blackjack:
def __init__(self, player: Player, dealer: Player, deck: Deck) -> None:
"""init"""
self.player = player
self.dealer = dealer
self.deck = deck
def _get_cards(se... | blackjack/blackjack.py | from blackjack.card import Card
from blackjack.deck import Deck
from blackjack.player import Player
class _Blackjack:
def __init__(self, player: Player, dealer: Player, deck: Deck) -> None:
"""init"""
self.player = player
self.dealer = dealer
self.deck = deck
def _get_cards(se... | 0.724481 | 0.163612 |
from __future__ import print_function, division, absolute_import
import os
import pytest
from sdss_brain import cfg_params
from sdss_brain.auth import Netrc
from sdss_brain.exceptions import BrainError
@pytest.fixture()
def netrc(monkeypatch, tmpdir):
tmpnet = tmpdir.mkdir('netrc').join('.netrc')
monkeypa... | tests/auth/test_netrc.py |
from __future__ import print_function, division, absolute_import
import os
import pytest
from sdss_brain import cfg_params
from sdss_brain.auth import Netrc
from sdss_brain.exceptions import BrainError
@pytest.fixture()
def netrc(monkeypatch, tmpdir):
tmpnet = tmpdir.mkdir('netrc').join('.netrc')
monkeypa... | 0.553023 | 0.205555 |
import os
import re
from .single import FileSinglePermission, _BaseVariables
class FileUserPermission(FileSinglePermission):
"""
Overview:
Single permission of the user part of a file.
Inherited from :class:`pysyslimit.models.permission.single.FileSinglePermission`.
With re... | pysyslimit/models/permission/full.py | import os
import re
from .single import FileSinglePermission, _BaseVariables
class FileUserPermission(FileSinglePermission):
"""
Overview:
Single permission of the user part of a file.
Inherited from :class:`pysyslimit.models.permission.single.FileSinglePermission`.
With re... | 0.734215 | 0.119229 |
try:
from os import makedirs
from shutil import copyfile
from os.path import join, exists
except ImportError as err:
exit(err)
if __name__ == "__main__":
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = "C:/Users/e_sgouge/Documents/Etienne/Pyt... | src/prepare_datasets/animals_data_preparation.py | try:
from os import makedirs
from shutil import copyfile
from os.path import join, exists
except ImportError as err:
exit(err)
if __name__ == "__main__":
# The path to the directory where the original
# dataset was uncompressed
original_dataset_dir = "C:/Users/e_sgouge/Documents/Etienne/Pyt... | 0.31237 | 0.319519 |
import enum
import os
import sys
from typing import Optional
import unittest
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
# pylint: disable=wrong-import-position
import deserialize
# pylint: enable=wrong-import-position
class SomeStringEnum(enum.Enum):
"""Enum example."""
... | tests/test_enums.py |
import enum
import os
import sys
from typing import Optional
import unittest
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
# pylint: disable=wrong-import-position
import deserialize
# pylint: enable=wrong-import-position
class SomeStringEnum(enum.Enum):
"""Enum example."""
... | 0.542863 | 0.245741 |
import numpy as np
class CameraIntr():
def __init__(self, u0, v0, fx, fy, sk=0, dtype=np.float32):
camera_xyz = np.array([
[fx, sk, u0],
[0, fy, v0],
[0, 0, 1],
], dtype=dtype).transpose()
pull_back_xyz = np.array([
[1 / fx, 0, -u0 / fx],
... | src/detector/graphics/camera.py | import numpy as np
class CameraIntr():
def __init__(self, u0, v0, fx, fy, sk=0, dtype=np.float32):
camera_xyz = np.array([
[fx, sk, u0],
[0, fy, v0],
[0, 0, 1],
], dtype=dtype).transpose()
pull_back_xyz = np.array([
[1 / fx, 0, -u0 / fx],
... | 0.770206 | 0.344581 |