text stringlengths 0 1.05M | meta dict |
|---|---|
__author__ = 'Steven'
from django.conf import settings
from django.contrib.auth.models import Permission, User
import csv, bcrypt
class Auth(object):
def authenticate(self, username=None, password=None):
try:
user = User.objects.get(username=username)
if user.check_password(passwor... | {
"repo_name": "steven-martins/Marking",
"path": "back/auth/blow.py",
"copies": "1",
"size": "1595",
"license": "mit",
"hash": 8827425271017534000,
"line_mean": 32.9574468085,
"line_max": 91,
"alpha_frac": 0.5611285266,
"autogenerated": false,
"ratio": 4.346049046321526,
"config_test": false,
... |
__author__ = 'steven'
from flask import Blueprint, jsonify, request
from sqlalchemy.exc import IntegrityError
from models import Project, Project_Student, Template, Task
import logging
import json
from datetime import datetime
task = Blueprint('task', __name__)
from api_tools import signed_auth, nocache
@task.rout... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api_blueprints/task.py",
"copies": "1",
"size": "6044",
"license": "mit",
"hash": 4006185749053383000,
"line_mean": 35.8536585366,
"line_max": 148,
"alpha_frac": 0.5918266049,
"autogenerated": false,
"ratio": 3.495662232504338,
"conf... |
__author__ = 'steven'
from flask import Blueprint, jsonify, request
from sqlalchemy.exc import IntegrityError
from models import User
import logging
user = Blueprint('user', __name__)
from api_tools import signed_auth, nocache
@user.route('/', methods=["GET"])
@signed_auth()
@nocache
def api_get_users():
from a... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api_blueprints/user.py",
"copies": "1",
"size": "4453",
"license": "mit",
"hash": -622508039411242100,
"line_mean": 31.503649635,
"line_max": 96,
"alpha_frac": 0.6099258927,
"autogenerated": false,
"ratio": 3.4734789391575664,
"confi... |
__author__ = 'steven'
from flask import Blueprint, jsonify, request, send_from_directory
from sqlalchemy.exc import IntegrityError
from models import Project, Project_Student, Template, Task, User
import logging
import json
from datetime import datetime
project = Blueprint('project', __name__)
from api_tools import ... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api_blueprints/project.py",
"copies": "1",
"size": "21073",
"license": "mit",
"hash": -7571091932764028000,
"line_mean": 38.0240740741,
"line_max": 125,
"alpha_frac": 0.5794618706,
"autogenerated": false,
"ratio": 3.7529830810329474,
... |
__author__ = 'steven'
from flask import request, make_response
from functools import wraps, update_wrapper
from functools import wraps
import config
import hashlib
import time
import datetime
import email.utils as eut
import pytz
import base64
import hmac
import logging
import csv
def intranet_auth():
def wrappe... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api_tools.py",
"copies": "1",
"size": "6000",
"license": "mit",
"hash": -6267960951511550000,
"line_mean": 39,
"line_max": 157,
"alpha_frac": 0.5305,
"autogenerated": false,
"ratio": 4.112405757368061,
"config_test": false,
"has_no... |
__author__ = 'steven'
from mixins.scm import GitMixin
from exceptions import RepositoryNameMissing
import logging
import os
import config
from api_tools import Mapping
mapping = Mapping()
class Pickup(GitMixin):
def __init__(self, task_id, project):
self._project = project
self._task_id = task_id... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "actions/pickup.py",
"copies": "1",
"size": "3330",
"license": "mit",
"hash": 6282610056759873000,
"line_mean": 50.2307692308,
"line_max": 118,
"alpha_frac": 0.5816816817,
"autogenerated": false,
"ratio": 4.215189873417722,
"config_te... |
__author__ = 'steven'
from sqlalchemy import create_engine
import config
from models import Task
from datetime import datetime
from datetime import timedelta
import logging
import time
from models import dump_datetime
engine = create_engine(config.SQL_DB_URI, echo=True, pool_recycle=3600)
from sqlalchemy.orm import s... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "scheduler.py",
"copies": "1",
"size": "1923",
"license": "mit",
"hash": -8945473579076291000,
"line_mean": 29.5238095238,
"line_max": 150,
"alpha_frac": 0.5481019241,
"autogenerated": false,
"ratio": 4.340857787810384,
"config_test":... |
__author__ = 'Steven'
import json
import hashlib
import random
try:
import httplib
except:
import http.client as httplib
import urllib
import uuid
import os
import pytz
import datetime
import base64
import hmac
import time
import config
class Client(object):
def __init__(self, host):
self._host... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "docs/api_client.py",
"copies": "1",
"size": "4064",
"license": "mit",
"hash": 5866118853153760000,
"line_mean": 35.2857142857,
"line_max": 158,
"alpha_frac": 0.5713582677,
"autogenerated": false,
"ratio": 3.4528462192013594,
"config_... |
__author__ = 'steven'
import os, errno, shutil, stat, logging
import config
import unicodedata
import string
from .execution import ExecMixin
validFilenameChars = "-_.()+ %s%s" % (string.ascii_letters, string.digits)
class FsMixin(ExecMixin):
def __init__(self):
pass
def _cleanfilename(self, filenam... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "mixins/fs.py",
"copies": "1",
"size": "5101",
"license": "mit",
"hash": 2540374530582915000,
"line_mean": 37.9389312977,
"line_max": 108,
"alpha_frac": 0.5428347383,
"autogenerated": false,
"ratio": 3.832456799398948,
"config_test": ... |
__author__ = 'steven'
import requests, json
from exceptions import NotImplemented, UnknownActivity
import config
import logging
import hashlib
import os
from api_tools import Mapping
mapping = Mapping()
class CrawlerMixin(object):
def __init__(self):
pass
def _bigint_json(self, data):
cleane... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "mixins/crawl.py",
"copies": "1",
"size": "8371",
"license": "mit",
"hash": 9072327539598562000,
"line_mean": 45.7653631285,
"line_max": 147,
"alpha_frac": 0.5302831203,
"autogenerated": false,
"ratio": 3.94300518134715,
"config_test"... |
__author__ = 'steven'
import subprocess, shlex
class ExecResult(object):
def __init__(self, return_code, outs, errs, exception=None):
self.return_code = return_code
self.outs = outs
self.errs = errs
self.exception = exception
class ExecMixin(object):
def __init__(self):
... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "mixins/execution.py",
"copies": "1",
"size": "1671",
"license": "mit",
"hash": 8166450829924128000,
"line_mean": 36.1333333333,
"line_max": 115,
"alpha_frac": 0.605625374,
"autogenerated": false,
"ratio": 3.9225352112676055,
"config_... |
__author__ = 'steven'
# mysql+mysqldb://<user>:<password>@<host>[:<port>]/<dbname>
from sqlalchemy import Column, String, Integer, ForeignKey, UniqueConstraint, Boolean, Enum, DateTime, Text, Table
from sqlalchemy.orm import relationship, backref
from sqlalchemy.ext.declarative import declarative_base
from config impo... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "models.py",
"copies": "1",
"size": "14274",
"license": "mit",
"hash": -1871919977932960800,
"line_mean": 39.3192090395,
"line_max": 136,
"alpha_frac": 0.5730400056,
"autogenerated": false,
"ratio": 3.8409580193756727,
"config_test": ... |
__author__ = 'Steven'
from django.core.management.base import BaseCommand, CommandError
try:
from back.marks.models import Project, Timeslot, User
except:
from marks.models import Project, Timeslot, User
import json, csv, io, os
class Load():
def __init__(self, csv_name):
self._rows = self._rea... | {
"repo_name": "steven-martins/Marking",
"path": "back/marks/management/commands/importprojects.py",
"copies": "1",
"size": "3031",
"license": "mit",
"hash": 8270243035897145000,
"line_mean": 30.9157894737,
"line_max": 92,
"alpha_frac": 0.5212801056,
"autogenerated": false,
"ratio": 4.245098039215... |
__author__ = 'steven'
from flask import Blueprint, jsonify, request
from sqlalchemy.exc import IntegrityError
import logging
from models import Template
template = Blueprint('template', __name__)
from api_tools import signed_auth,nocache
@template.route('/', methods=["GET"])
@signed_auth()
@nocache
def api_get_te... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api_blueprints/template.py",
"copies": "1",
"size": "3641",
"license": "mit",
"hash": -4725117468301885000,
"line_mean": 32.712962963,
"line_max": 91,
"alpha_frac": 0.6146663005,
"autogenerated": false,
"ratio": 3.511089681774349,
"c... |
__author__ = 'steven'
from flask import request, Flask, jsonify
from flask_sqlalchemy import SQLAlchemy
from flask_cors import CORS
import config
from api_tools import nocache
app = Flask(__name__)
app.config['SQLALCHEMY_DATABASE_URI'] = config.SQL_DB_URI
app.config['SQLALCHEMY_POOL_RECYCLE'] = 3600
try:
if con... | {
"repo_name": "steven-martins/ramassage.epitech.eu",
"path": "api.py",
"copies": "1",
"size": "1642",
"license": "mit",
"hash": -7866926175499135000,
"line_mean": 25.4838709677,
"line_max": 124,
"alpha_frac": 0.6747868453,
"autogenerated": false,
"ratio": 3.2514851485148513,
"config_test": fals... |
__author__ = 'steve'
""" Read all matching log files in specified folder, extracting the contents into a usable model, """
import glob, re, gzip
from portality.core import app
from portality.models import SshEntry
from datetime import datetime
# Regular Expressions to get the relevant lines. We only want sshd lines l... | {
"repo_name": "Steven-Eardley/ssh_attacks",
"path": "portality/read_logs.py",
"copies": "1",
"size": "3931",
"license": "mit",
"hash": 6940079315688163000,
"line_mean": 33.1826086957,
"line_max": 101,
"alpha_frac": 0.6481811244,
"autogenerated": false,
"ratio": 3.691079812206573,
"config_test":... |
__author__ = 'stevet'
import re
import time
import inspect
from maya import cmds
from mGui import gui, forms, lists
from mGui.bindings import bind
from mGui.observable import ViewCollection
from mGui.qt.QTextField import QTextField
from mGui.scriptJobs import Idle
"""
This example illustrates the optional QTextField... | {
"repo_name": "theodox/mGui",
"path": "mGui/examples/filtered_collection.py",
"copies": "1",
"size": "2353",
"license": "mit",
"hash": -1389931252412136200,
"line_mean": 26.3604651163,
"line_max": 97,
"alpha_frac": 0.5954101147,
"autogenerated": false,
"ratio": 3.7468152866242037,
"config_test"... |
__author__ = 'stevet'
import sys
import traceback
from maya.api.OpenMaya import MFnPlugin, MPxCommand, MSyntax, MDGModifier, MArgDatabase, MGlobal, MDagModifier, \
MDistance, MAngle, MTime
__version__ = 0.5
class initializePlugin2(object):
"""
Proxies the `initializePlugin` method that Maya expects to b... | {
"repo_name": "theodox/plugger",
"path": "plugger/__init__.py",
"copies": "1",
"size": "7948",
"license": "mit",
"hash": 8895601633029040000,
"line_mean": 29.5692307692,
"line_max": 120,
"alpha_frac": 0.611348767,
"autogenerated": false,
"ratio": 4.298539751216874,
"config_test": false,
"has_... |
__author__ = 'steve_w'
from urllib2 import urlopen
from bs4 import BeautifulSoup
def get_locations(url):
"""
Function gets the url of yum website and gets the locations of the restuarants and convert them to linkable url
:param url: link to yum webiste
:return: list of locations
"""
soup = Be... | {
"repo_name": "SteveWaweru/yumscrap",
"path": "yumscrap.py",
"copies": "1",
"size": "2691",
"license": "mit",
"hash": -997303097672517200,
"line_mean": 37.4571428571,
"line_max": 200,
"alpha_frac": 0.652173913,
"autogenerated": false,
"ratio": 3.536136662286465,
"config_test": false,
"has_no_... |
__author__ = 'steve_w'
import random
class GeneticAlgorithm(object):
def __init__(self, genetics):
self.genetics = genetics
pass
def run(self):
population = self.genetics.initial()
while True:
fits_pops = [(self.genetics.fitness(ch), ch) for ch in population]
... | {
"repo_name": "TonyHinjos/Machine-Learning-Algorithms-Toolkit",
"path": "Genetic Algorithm /Genetic Algorithm.py",
"copies": "2",
"size": "5561",
"license": "mit",
"hash": -3289175141756668400,
"line_mean": 31.1445086705,
"line_max": 79,
"alpha_frac": 0.548282683,
"autogenerated": false,
"ratio":... |
__authors__ = ['Thomas Bass']
## Candidate Number 4869 | Centre Number 52423
## TASK 2
import sqlite3 as lite ## Imports libraries
import random
import math
currentOrder = [] ## Define curr... | {
"repo_name": "electric-blue-green/GSCE-Coursework-GTIN",
"path": "Final Compiled/Task 2/task2.py",
"copies": "2",
"size": "8038",
"license": "apache-2.0",
"hash": -8777599494856201000,
"line_mean": 90.3563218391,
"line_max": 165,
"alpha_frac": 0.4017179136,
"autogenerated": false,
"ratio": 5.209... |
__authors__ = ['Thomas Bass']
## Candidate Number 4869 | Centre Number 52423
## TASK 2
import sqlite3 as lite
import random
import math
currentOrder = []
con = lite.connect('dbuse.db')
cur = con.cursor()
def verify(con, cur, currentOrder):
var = input('Enter GTIN for the product you wish to purchase:\... | {
"repo_name": "electric-blue-green/GSCE-Coursework-GTIN",
"path": "Task 2/FINAL/task2 Development Testing.py",
"copies": "1",
"size": "3603",
"license": "apache-2.0",
"hash": 9088495723665561000,
"line_mean": 39.3908045977,
"line_max": 165,
"alpha_frac": 0.5942793668,
"autogenerated": false,
"rat... |
import time
import numpy as np
from scipy import sparse
from .lil import is_lil
from .. import cython_code
from . import check_random_state
from ..loss_and_gradient import gradient_zi
from .convolution import _choose_convolve_multi
def _coordinate_descent_idx(Xi, D, constants, reg, z0=None, max_iter=1000,
... | {
"repo_name": "alphacsc/alphacsc",
"path": "alphacsc/utils/coordinate_descent.py",
"copies": "1",
"size": "10052",
"license": "bsd-3-clause",
"hash": -490154152721771800,
"line_mean": 34.0243902439,
"line_max": 79,
"alpha_frac": 0.5393951452,
"autogenerated": false,
"ratio": 3.287115761935906,
... |
__author__ = 'stig'
import argparse
import sys
import numpy as np
from pymjolnir.mjolnir import Mjolnir
def command_line_parse(default_concurrencies):
parser = argparse.ArgumentParser(description='Wraps "ab" (Apache HTTP server benchmarking tool) to '
'load test we... | {
"repo_name": "stiggg/pymjolnir",
"path": "src/app.py",
"copies": "1",
"size": "1180",
"license": "mit",
"hash": -1469287906804664000,
"line_mean": 32.7428571429,
"line_max": 112,
"alpha_frac": 0.6322033898,
"autogenerated": false,
"ratio": 3.5435435435435436,
"config_test": false,
"has_no_ke... |
__author__ = 'stig'
import envoy
import re
import numpy as np
import sys
class Mjolnir():
REQUEST_MULTIPLIER = 10
def strike(self, url, concurrencies):
means = []
stds = []
for concurrency in concurrencies:
requests = concurrency * self.REQUEST_MULTIPLIER
sel... | {
"repo_name": "stiggg/pymjolnir",
"path": "src/pymjolnir/mjolnir.py",
"copies": "1",
"size": "2023",
"license": "mit",
"hash": 8316275756845901000,
"line_mean": 27.1111111111,
"line_max": 145,
"alpha_frac": 0.5669797331,
"autogenerated": false,
"ratio": 3.651624548736462,
"config_test": false,
... |
__author__ = 'Stojan Jovic <stojan.jovic@dmsgroup.rs>'
__contact__ = 'stojan.jovic@dmsgroup.rs'
__date__ = '04 February 2009'
__copyright__ = 'Copyright (c) 2008 DMS Group'
import socket
import logging
import xmllayout
# Adding custom debug levels (for example: TRACE, i.e. VERBOSE)
logging.VERBOSE... | {
"repo_name": "nickswebsite/pylogfaces",
"path": "logFaces_logger_example.py",
"copies": "1",
"size": "2076",
"license": "bsd-3-clause",
"hash": -5630117559583431000,
"line_mean": 29.9384615385,
"line_max": 81,
"alpha_frac": 0.7003853565,
"autogenerated": false,
"ratio": 3.2136222910216716,
"co... |
import matplotlib.pyplot as plt
from joblib import Memory
import numpy as np
import gc
import time
from sklearn.linear_model import (LogisticRegression, SGDClassifier)
from sklearn.datasets import fetch_rcv1
from sklearn.linear_model._sag import get_auto_step_size
try:
import lightning.classification as lightnin... | {
"repo_name": "ndingwall/scikit-learn",
"path": "benchmarks/bench_rcv1_logreg_convergence.py",
"copies": "18",
"size": "7212",
"license": "bsd-3-clause",
"hash": 1250481961454503200,
"line_mean": 29.3025210084,
"line_max": 79,
"alpha_frac": 0.5818080976,
"autogenerated": false,
"ratio": 3.1479703... |
import matplotlib.pyplot as plt
import numpy as np
import gc
import time
from sklearn.externals.joblib import Memory
from sklearn.linear_model import (LogisticRegression, SGDClassifier)
from sklearn.datasets import fetch_rcv1
from sklearn.linear_model.sag import get_auto_step_size
try:
import lightning.classific... | {
"repo_name": "pprett/scikit-learn",
"path": "benchmarks/bench_rcv1_logreg_convergence.py",
"copies": "58",
"size": "7229",
"license": "bsd-3-clause",
"hash": 8959127179930633000,
"line_mean": 29.3739495798,
"line_max": 79,
"alpha_frac": 0.5826532024,
"autogenerated": false,
"ratio": 3.1498910675... |
import matplotlib.pyplot as plt
import numpy as np
import gc
import time
from sklearn.utils import Memory
from sklearn.linear_model import (LogisticRegression, SGDClassifier)
from sklearn.datasets import fetch_rcv1
from sklearn.linear_model.sag import get_auto_step_size
try:
import lightning.classification as li... | {
"repo_name": "vortex-ape/scikit-learn",
"path": "benchmarks/bench_rcv1_logreg_convergence.py",
"copies": "7",
"size": "7218",
"license": "bsd-3-clause",
"hash": 3917219947076932600,
"line_mean": 29.3277310924,
"line_max": 79,
"alpha_frac": 0.5821557218,
"autogenerated": false,
"ratio": 3.1492146... |
class Prime:
"""Provides methods to obtain prime numbers and use them."""
def __init__(self):
pass
# Cache for already calculated prime numbers
cache = [2, 3]
@staticmethod
def get_prime(index):
"""
Returns the prime number at the given index. The index starts with 0... | {
"repo_name": "Koopakiller/School",
"path": "NLA/serie1/prime.py",
"copies": "2",
"size": "2370",
"license": "mit",
"hash": -6522836265696626000,
"line_mean": 28.2592592593,
"line_max": 107,
"alpha_frac": 0.5202531646,
"autogenerated": false,
"ratio": 4.065180102915952,
"config_test": false,
... |
# License: BSD (3-clause)
import os.path as op
import warnings
import copy
import numpy as np
from scipy import sparse, linalg
from .fixes import _get_img_fdata
from .parallel import parallel_func
from .source_estimate import (
_BaseSurfaceSourceEstimate, _BaseVolSourceEstimate, _BaseSourceEstimate,
_get_ico... | {
"repo_name": "olafhauk/mne-python",
"path": "mne/morph.py",
"copies": "4",
"size": "59960",
"license": "bsd-3-clause",
"hash": 9222653154321270000,
"line_mean": 40.955913226,
"line_max": 102,
"alpha_frac": 0.5858727379,
"autogenerated": false,
"ratio": 3.616539992761491,
"config_test": false,
... |
# License: BSD (3-clause)
import os.path as op
import warnings
import copy
import numpy as np
from scipy import sparse
from .fixes import _get_img_fdata
from .parallel import parallel_func
from .source_estimate import (
VolSourceEstimate, _BaseSurfaceSourceEstimate,
_BaseVolSourceEstimate, _BaseSourceEstimat... | {
"repo_name": "cjayb/mne-python",
"path": "mne/morph.py",
"copies": "1",
"size": "56407",
"license": "bsd-3-clause",
"hash": -2649228916433032700,
"line_mean": 39.90137781,
"line_max": 102,
"alpha_frac": 0.5892771661,
"autogenerated": false,
"ratio": 3.595066607177003,
"config_test": false,
"... |
# License: BSD (3-clause)
import os.path as op
import warnings
import copy
import numpy as np
from scipy import sparse
from .parallel import parallel_func
from .source_estimate import (VolSourceEstimate, SourceEstimate,
VolVectorSourceEstimate, VectorSourceEstimate,
... | {
"repo_name": "adykstra/mne-python",
"path": "mne/morph.py",
"copies": "1",
"size": "49187",
"license": "bsd-3-clause",
"hash": -2604674499334659600,
"line_mean": 39.6504132231,
"line_max": 102,
"alpha_frac": 0.5895053571,
"autogenerated": false,
"ratio": 3.7125066042720207,
"config_test": fals... |
# License: BSD (3-clause)
import os.path as op
import warnings
import copy
import numpy as np
from .fixes import _get_img_fdata
from .morph_map import read_morph_map
from .parallel import parallel_func
from .source_estimate import (
_BaseSurfaceSourceEstimate, _BaseVolSourceEstimate, _BaseSourceEstimate,
_ge... | {
"repo_name": "bloyl/mne-python",
"path": "mne/morph.py",
"copies": "1",
"size": "60555",
"license": "bsd-3-clause",
"hash": 6180334274734830000,
"line_mean": 40.8445058742,
"line_max": 102,
"alpha_frac": 0.5851128838,
"autogenerated": false,
"ratio": 3.6163769933703636,
"config_test": false,
... |
# License: BSD (3-clause)
import os.path as op
import warnings
import copy
import numpy as np
from .fixes import _get_img_fdata
from .parallel import parallel_func
from .source_estimate import (
_BaseSurfaceSourceEstimate, _BaseVolSourceEstimate, _BaseSourceEstimate,
_get_ico_tris)
from .source_space import ... | {
"repo_name": "wmvanvliet/mne-python",
"path": "mne/morph.py",
"copies": "2",
"size": "60065",
"license": "bsd-3-clause",
"hash": -4943030793413473000,
"line_mean": 40.912072575,
"line_max": 102,
"alpha_frac": 0.5860972361,
"autogenerated": false,
"ratio": 3.6180722891566264,
"config_test": fal... |
import icons
import os
import sys
import re
import time
#import urllib2 // for updater next version
#import json // for updater next version
from time import time, sleep
from PyQt5.QtGui import QIcon
from PyQt5.QtCore import QProcess
from PyQt5 import QtCore, QtGui, QtWidgets
from PyQt5.QtGui import QFont, QPixmap
from... | {
"repo_name": "techbliss/Windows_Screenrecorder",
"path": "Build folder/rec_main.py",
"copies": "1",
"size": "44395",
"license": "mit",
"hash": -1420300660222871300,
"line_mean": 48.8260381594,
"line_max": 195,
"alpha_frac": 0.3683522919,
"autogenerated": false,
"ratio": 4.520875763747454,
"con... |
__author__ = 'stowellc17'
from pygext.notifier import global_notify
class Messenger:
notify = global_notify.new_category('Messenger')
def __init__(self):
self._acceptors = {}
def accept(self, object, event, method):
if event not in self._acceptors:
self._acceptors[event] = ... | {
"repo_name": "chandler14362/pygext",
"path": "pygext/messenger.py",
"copies": "1",
"size": "1674",
"license": "mit",
"hash": 5911106447354976000,
"line_mean": 29.4363636364,
"line_max": 113,
"alpha_frac": 0.5818399044,
"autogenerated": false,
"ratio": 4.043478260869565,
"config_test": false,
... |
__author__ = 'stowellc17'
from twisted.internet.error import AlreadyCalled
from twisted.internet.task import LoopingCall
from twisted.internet import reactor
from pygext.notifier import global_notify
TASK_DONE = 0
TASK_AGAIN = 1
class Task:
notify = global_notify.new_category('Task')
def __init__(self, ... | {
"repo_name": "chandler14362/pygext",
"path": "pygext/taskmanager.py",
"copies": "1",
"size": "2806",
"license": "mit",
"hash": 1041728283480147100,
"line_mean": 24.2792792793,
"line_max": 99,
"alpha_frac": 0.5727013542,
"autogenerated": false,
"ratio": 3.9080779944289694,
"config_test": false,... |
__author__ = 'stowellc17'
LEVEL_DEBUG = 0
LEVEL_INFO = 1
LEVEL_WARNING = 2
LEVEL_ERROR = 3
class NotifyCategory:
def __init__(self, notify, name):
self.notify = notify
self.name = name
def debug(self, message):
if self.notify.can_output(LEVEL_DEBUG):
print('|DEBUG| %s: %... | {
"repo_name": "chandler14362/pygext",
"path": "pygext/notifier.py",
"copies": "1",
"size": "1064",
"license": "mit",
"hash": -1351914250008226000,
"line_mean": 23.7441860465,
"line_max": 60,
"alpha_frac": 0.587406015,
"autogenerated": false,
"ratio": 3.432258064516129,
"config_test": false,
"... |
"""
Base classes for MATLAB file stream reading.
MATLAB is a registered trademark of the Mathworks inc.
"""
from __future__ import division, print_function, absolute_import
import operator
import functools
import numpy as np
from scipy._lib import doccer
from . import byteordercodes as boc
class MatReadError(Exc... | {
"repo_name": "arokem/scipy",
"path": "scipy/io/matlab/miobase.py",
"copies": "1",
"size": "12006",
"license": "bsd-3-clause",
"hash": -8558860502057887000,
"line_mean": 28.4987714988,
"line_max": 80,
"alpha_frac": 0.6158587373,
"autogenerated": false,
"ratio": 3.7754716981132077,
"config_test"... |
"""
Base classes for MATLAB file stream reading.
MATLAB is a registered trademark of the Mathworks inc.
"""
from __future__ import division, print_function, absolute_import
import operator
import sys
import numpy as np
from scipy._lib.six import reduce
if sys.version_info[0] >= 3:
byteord = int
else:
byteo... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scipy-master/scipy/io/matlab/miobase.py",
"copies": "1",
"size": "12089",
"license": "mit",
"hash": -8975930575358711000,
"line_mean": 28.1301204819,
"line_max": 80,
"alpha_frac": 0.6156009596,
"autogenerated": false,
"ratio": 3.764870... |
"""
Base classes for MATLAB file stream reading.
MATLAB is a registered trademark of the Mathworks inc.
"""
from __future__ import division, print_function, absolute_import
import sys
import numpy as np
if sys.version_info[0] >= 3:
byteord = int
else:
byteord = ord
from scipy.misc import doccer
from . imp... | {
"repo_name": "Universal-Model-Converter/UMC3.0a",
"path": "data/Python/x86/Lib/site-packages/scipy/io/matlab/miobase.py",
"copies": "3",
"size": "11706",
"license": "mit",
"hash": -6567999490765558000,
"line_mean": 28.4120603015,
"line_max": 75,
"alpha_frac": 0.6105416026,
"autogenerated": false,
... |
"""
Base classes for MATLAB file stream reading.
MATLAB is a registered trademark of the Mathworks inc.
"""
from __future__ import division, print_function, absolute_import
import sys
import operator
from scipy._lib.six import reduce
import numpy as np
if sys.version_info[0] >= 3:
byteord = int
else:
byte... | {
"repo_name": "jamestwebber/scipy",
"path": "scipy/io/matlab/miobase.py",
"copies": "1",
"size": "12104",
"license": "bsd-3-clause",
"hash": -2811247989348991500,
"line_mean": 28.1662650602,
"line_max": 80,
"alpha_frac": 0.6159947125,
"autogenerated": false,
"ratio": 3.762511656823127,
"config_... |
"""
Base classes for MATLAB file stream reading.
MATLAB is a registered trademark of the Mathworks inc.
"""
import operator
import functools
import numpy as np
from scipy._lib import doccer
from . import byteordercodes as boc
class MatReadError(Exception):
pass
class MatWriteError(Exception):
pass
cla... | {
"repo_name": "person142/scipy",
"path": "scipy/io/matlab/miobase.py",
"copies": "3",
"size": "11940",
"license": "bsd-3-clause",
"hash": -2171715887152351700,
"line_mean": 28.4814814815,
"line_max": 80,
"alpha_frac": 0.6149916248,
"autogenerated": false,
"ratio": 3.7737041719342606,
"config_te... |
"""
Base classes for matlab (TM) file stream reading
"""
import warnings
import numpy as np
from scipy.ndimage import doccer
import byteordercodes as boc
class MatReadError(Exception): pass
doc_dict = \
{'file_arg':
'''file_name : string
Name of the mat file (do not need .mat extension if
appen... | {
"repo_name": "huard/scipy-work",
"path": "scipy/io/matlab/miobase.py",
"copies": "1",
"size": "18919",
"license": "bsd-3-clause",
"hash": -6131410916078749000,
"line_mean": 32.6039076377,
"line_max": 83,
"alpha_frac": 0.5849146361,
"autogenerated": false,
"ratio": 4.027032779906343,
"config_te... |
"""
Module for reading and writing matlab (TM) .mat files
"""
import os
import sys
import warnings
from miobase import get_matfile_version, docfiller
from mio4 import MatFile4Reader, MatFile4Writer
from mio5 import MatFile5Reader, MatFile5Writer
__all__ = ['find_mat_file', 'mat_reader_factory', 'loadmat', 'savemat'... | {
"repo_name": "huard/scipy-work",
"path": "scipy/io/matlab/mio.py",
"copies": "1",
"size": "5397",
"license": "bsd-3-clause",
"hash": -1741612395532932900,
"line_mean": 30.7470588235,
"line_max": 86,
"alpha_frac": 0.5853251807,
"autogenerated": false,
"ratio": 3.747916666666667,
"config_test": ... |
"""This module allows for the loading of an array from an ASCII
Text File
"""
__all__ = ['read_array', 'write_array']
# Standard library imports.
import os
import re
import sys
import types
# Numpy imports.
import numpy
from numpy import array, take, concatenate, asarray, real, imag, \
deprecate_with_doc
# Sadl... | {
"repo_name": "huard/scipy-work",
"path": "scipy/io/array_import.py",
"copies": "1",
"size": "17528",
"license": "bsd-3-clause",
"hash": -560847653054485600,
"line_mean": 34.1967871486,
"line_max": 96,
"alpha_frac": 0.5708580557,
"autogenerated": false,
"ratio": 3.871024734982332,
"config_test"... |
__author__ = 'stuart'
from collections import defaultdict
from ._join_funcs import union_join, tuple_join, make_union_join
def merge(left, right, how='inner', key=None, left_key=None, right_key=None,
left_as='left', right_as='right'):
""" Performs a join using the union join function. """
return jo... | {
"repo_name": "StuartAxelOwen/join",
"path": "join/_core.py",
"copies": "1",
"size": "5900",
"license": "mit",
"hash": 8079025246180780000,
"line_mean": 37.3116883117,
"line_max": 131,
"alpha_frac": 0.6461016949,
"autogenerated": false,
"ratio": 3.617412630288167,
"config_test": false,
"has_n... |
__author__ = 'stuart'
from datastreams import DataStream
from datastreams import join_objects
from itertools import product
class RddStream(DataStream):
def __init__(self, source_rdd):
self._source = source_rdd
@staticmethod
def Stream(rdd):
return RddStream(rdd)
def map(self, func... | {
"repo_name": "StuartAxelOwen/datastreams",
"path": "datastreams/rddstreams.py",
"copies": "1",
"size": "3809",
"license": "mit",
"hash": 3585393981364712000,
"line_mean": 31.2796610169,
"line_max": 91,
"alpha_frac": 0.6266736676,
"autogenerated": false,
"ratio": 3.786282306163022,
"config_test... |
__author__ = 'stuart'
import os, sys, inspect
currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
parentdir = os.path.dirname(currentdir)
sys.path.insert(0,parentdir)
from datastreams import DataSet, DataStream, Datum, DictSet, DictStream
if sys.version_info[0] == 2 and sys.version_... | {
"repo_name": "StuartAxelOwen/datastreams",
"path": "test/test_datastreams.py",
"copies": "1",
"size": "15338",
"license": "mit",
"hash": 8238453935962712000,
"line_mean": 35.345971564,
"line_max": 91,
"alpha_frac": 0.5896466293,
"autogenerated": false,
"ratio": 3.5211202938475665,
"config_test... |
__author__ = 'Subhashis'
import random
import Game
from copy import copy
# Currently for 2P mode only
class CRAIController(Game.Controller):
def __init__(self, max_depth=3):
self.max_depth = max_depth
def make_move(self, state):
print "Waiting for player " + str(state.current_player) + "..."... | {
"repo_name": "subhashisbhowmik/CRAI",
"path": "CRAI.py",
"copies": "1",
"size": "3043",
"license": "apache-2.0",
"hash": 6274996389910330000,
"line_mean": 33.9770114943,
"line_max": 117,
"alpha_frac": 0.5090371344,
"autogenerated": false,
"ratio": 4.20303867403315,
"config_test": false,
"has... |
__author__ = "Subhav Pradhan"
import operator
import datetime
import copy
from operator import attrgetter
from chariot_helpers import Serialize
from logger import get_logger
logger = get_logger("solver_backend")
class GoalDescription:
name = None
replicationConstraints = None # List of constraints ... | {
"repo_name": "visor-vu/chariot",
"path": "Runtime/chariot_runtime_libs/solver_backend.py",
"copies": "2",
"size": "79832",
"license": "mit",
"hash": 5990528472515837000,
"line_mean": 50.940143136,
"line_max": 199,
"alpha_frac": 0.6225573705,
"autogenerated": false,
"ratio": 5.242792408222237,
... |
__author__ = "Subhav Pradhan"
import os
import logging
# Helper to create and return python logger. This uses environment variable CHARIOT_LOG_LEVEL to determine base log level.
def get_logger(name):
logger = logging.getLogger(name)
# Get base log level from environment variable.
try:
logLeve... | {
"repo_name": "dcpssc/chariot",
"path": "Runtime/chariot_runtime_libs/logger.py",
"copies": "2",
"size": "1125",
"license": "mit",
"hash": -855127245804752500,
"line_mean": 30.25,
"line_max": 122,
"alpha_frac": 0.6488888889,
"autogenerated": false,
"ratio": 4.090909090909091,
"config_test": fal... |
__author__ = "Subhav Pradhan"
import os, signal, subprocess
import re
from random import randint
from chariot_helpers import Serialize
from logger import get_logger
logger = get_logger("deployment_manager")
def execute_start_action(actionProcess, actionStartScript):
retval = None
env_str = os.getenv('APP_HO... | {
"repo_name": "dcpssc/chariot",
"path": "Runtime/chariot_runtime_libs/deployment_manager.py",
"copies": "2",
"size": "5595",
"license": "mit",
"hash": 5066955502038514000,
"line_mean": 41.7099236641,
"line_max": 140,
"alpha_frac": 0.6253798034,
"autogenerated": false,
"ratio": 4.07798833819242,
... |
__author__ = "Subhav Pradhan"
import time
import socket, zmq, json
import copy, re
from solver_backend import SolverBackend
from new_configuration_solver_bound import NewConfigurationSolverBound
from chariot_helpers import Serialize, get_node_address
from deployment_manager import update_start_action, update_stop_acti... | {
"repo_name": "dcpssc/chariot",
"path": "Runtime/chariot_runtime_libs/management_engine.py",
"copies": "2",
"size": "19410",
"license": "mit",
"hash": 1424074315726310100,
"line_mean": 40.8318965517,
"line_max": 130,
"alpha_frac": 0.591241628,
"autogenerated": false,
"ratio": 4.7008961007507875,
... |
__author__ = "Subhav Pradhan, Tihamer Levendovszky"
# Base class file for all solvers. General encoding.
from z3 import *
from logger import get_logger
logger = get_logger("configuration_solver")
class ConfigurationSolver(object):
def __init__(self,
NO_OF_NODES,
NO_OF_COMPONEN... | {
"repo_name": "visor-vu/chariot",
"path": "Runtime/chariot_runtime_libs/configuration_solver.py",
"copies": "2",
"size": "20105",
"license": "mit",
"hash": -1785656469056033500,
"line_mean": 42.6117136659,
"line_max": 166,
"alpha_frac": 0.6243223079,
"autogenerated": false,
"ratio": 3.83829705994... |
__author__ = 'Sudhanshu Patel'
from xlrd import open_workbook
import codecs
import sys
import time
def remove_anomaly(s):
#Remove or replace data with ascii value greter than 128
cell_data=''
for ch in s:
if ord(ch) <127:
cell_data +=ch
return cell_data
if __nam... | {
"repo_name": "Hack22learn/python-Application",
"path": "Email_Harvesting/Python_code/Excell Access/Excel_EModifire.py",
"copies": "2",
"size": "2890",
"license": "mit",
"hash": 7581172646396704000,
"line_mean": 31.6046511628,
"line_max": 109,
"alpha_frac": 0.453633218,
"autogenerated": false,
"r... |
__author__ = 'Sudhanshu Patel'
import urllib2
import os
class EHarvestor():
def __init__(self,url,fname):
self.url=url
self.data=''
self.file=fname
self.counter=0
def get_data(self):
# Get data from web
'''
connect to given url and store... | {
"repo_name": "Hack22learn/Small-Application---Python",
"path": "Email_Harvesting/Python_code/harvester/EmHarvestor.py",
"copies": "2",
"size": "1943",
"license": "mit",
"hash": -274305492946541220,
"line_mean": 27.4393939394,
"line_max": 73,
"alpha_frac": 0.4503345342,
"autogenerated": false,
"r... |
__author__ = "Sudip Sinha"
from cliquet import cliquet_sp
def run_cliquet_high(ms: list, d: int = 9) -> None:
"""Display short results for a list of 'n's."""
for m in ms:
pr = cliquet_sp( r = 0.03, q = 0., sigma = 0.2,
# sigma = [(0.05 + 0.04 * i) for i in range(1,9)],
t = ... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/runCliquet.py",
"copies": "1",
"size": "1366",
"license": "mit",
"hash": 6656690291073117000,
"line_mean": 34.9473684211,
"line_max": 72,
"alpha_frac": 0.4311859444,
"autogenerated": false,
"ratio": 2.511029411764706,
"config_test": ... |
__author__ = "Sudip Sinha"
from math import exp, sqrt
def vanilla_call(r: float, # Market
s0: float, sigma: float, q: float, # Underlying
k: float, t: float, am: bool=True, # Derivative
n: int=25 # Computation
) -> list:
"""Price of a Am... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/asian_binom.py",
"copies": "1",
"size": "1153",
"license": "mit",
"hash": -9147303423177553000,
"line_mean": 29.3421052632,
"line_max": 82,
"alpha_frac": 0.4770164788,
"autogenerated": false,
"ratio": 2.447983014861996,
"config_test"... |
__author__ = "Sudip Sinha"
from math import exp, sqrt
# @profile
def sp_asian_call_old(r: float, # Market
s0: float, sigma: float, q: float, # Underlying
k: float, t: float, am: bool=False, # Derivative
mach_eps=65536 * (7/3 - 4/3 - 1), n: int=25, h: flo... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/tr_asian_singularpoints_old.py",
"copies": "1",
"size": "6312",
"license": "mit",
"hash": 5223741569140710000,
"line_mean": 31.2040816327,
"line_max": 106,
"alpha_frac": 0.4218948035,
"autogenerated": false,
"ratio": 2.0374435119431893... |
__author__ = "Sudip Sinha"
from math import exp, sqrt
# @profile
def sp_asian_call(r: float, # Market
s0: float, sigma: float, q: float, # Underlying
k: float, t: float, am: bool=False, # Derivative
mach_eps=65536 * (7/3 - 4/3 - 1), n: int=25, h: float=0... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/tr_asian_geometric_singularpoints.py",
"copies": "1",
"size": "1830",
"license": "mit",
"hash": -3834633546892695600,
"line_mean": 26.7272727273,
"line_max": 106,
"alpha_frac": 0.4568306011,
"autogenerated": false,
"ratio": 2.084282460... |
__author__ = "Sudip Sinha"
from math import exp, sqrt
# @profile
def sp_asian_call(s0: float, sigma: float, q: float, # Underlying
k: float, t: float, am: bool=True, # Derivative
n: int=16, h: float=0., ub: bool=True # Computation
) -> list:
"""Prices o... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/SPAsianOld.py",
"copies": "1",
"size": "6035",
"license": "mit",
"hash": -7561050043754738000,
"line_mean": 30.7631578947,
"line_max": 109,
"alpha_frac": 0.4241922121,
"autogenerated": false,
"ratio": 2.0278897849462365,
"config_test... |
__author__ = "Sudip Sinha"
from tr_crr import tr_underlying
from tr_vanilla import vanilla_call
from asian import asian_call_sp
from tr_asian_singularpoints_old import sp_asian_call_old
# http://www.goddardconsulting.ca/matlab-binomial-crr.html
# http://www.hoadley.net/options/binomialtree.aspx?tree=B
# http://www.m... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Thesis/code/runAsian.py",
"copies": "1",
"size": "3624",
"license": "mit",
"hash": -3628707790591341000,
"line_mean": 41.1395348837,
"line_max": 109,
"alpha_frac": 0.5560154525,
"autogenerated": false,
"ratio": 2.057921635434412,
"config_test": ... |
__author__ = 'Sudip Sinha'
import math
# Computer
eps = 256 * (7/3 - 4/3 - 1)
# Market
r = 0.07
# Underlying
s0 = 100.0
sigma = 0.2
# Derivative
T = 1.0
k = 90.0
def pos(x):
return x if (x > 0) else 0
def getTree(s0, sigma, n, t):
"""Generate the tree of stock prices"""
s = [[0]*(i+1) for i in range(n+1)]
... | {
"repo_name": "SudipSinha/edu",
"path": "MathMods/Finance/SPEuropeanAsianOptions.py",
"copies": "1",
"size": "5145",
"license": "mit",
"hash": 3093739599034187300,
"line_mean": 28.7398843931,
"line_max": 113,
"alpha_frac": 0.4864917396,
"autogenerated": false,
"ratio": 2.1017156862745097,
"conf... |
__author__ = 'suidov'
import numpy as np
from sklearn.preprocessing import normalize
def toList(filename):
popList = []
with open(filename) as file:
for line in file:
strList = line.split()
floatList = []
for element in strList:
floatList.append(floa... | {
"repo_name": "lkokhreidze/cg-project",
"path": "tools/Tools.py",
"copies": "1",
"size": "2845",
"license": "apache-2.0",
"hash": 6659078437142272000,
"line_mean": 28.0306122449,
"line_max": 113,
"alpha_frac": 0.5574692443,
"autogenerated": false,
"ratio": 3.122941822173436,
"config_test": fals... |
__author__ = 'Sukrit'
import bson
import pandas as pd
import numpy as np
#import matplotlib.pyplot as plt
#from scipy.optimize import curve_fit
ELElist = []
with open('../data/Elsevier_journal_list.csv', 'r') as file :
x = file.readlines()
for line in x :
#print line
line = line.replace('&','a... | {
"repo_name": "SciBase-Project/internationality-journals",
"path": "src/get_journal_list_Aminer.py",
"copies": "3",
"size": "1586",
"license": "mit",
"hash": 5098038992123449000,
"line_mean": 20.7260273973,
"line_max": 89,
"alpha_frac": 0.6223203026,
"autogenerated": false,
"ratio": 3.41810344827... |
__author__ = 'Sukrit'
import pandas as pd
import csv
f = open('../output/both_journal_list.txt', 'r') #reading list of journals present in Aminer and Elesevier
x = f.readlines()
f.close()
bothjs = []
for line in x:
bothjs.append(line.rstrip()) # list of common journals, removing '\n'
# OUR SNIP
our_SNIP = pd.r... | {
"repo_name": "sujithvm/internationality-journals",
"path": "src/SNIPvsourSNIPv2.py",
"copies": "3",
"size": "2366",
"license": "mit",
"hash": 2799367627204670500,
"line_mean": 24.1808510638,
"line_max": 153,
"alpha_frac": 0.6538461538,
"autogenerated": false,
"ratio": 2.646532438478747,
"confi... |
__author__ = 'Sukrit'
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from scipy.optimize import curve_fit
def poly_fit(x,y,deg):
#POLYNOMIAL FIT
# calculate polynomial
z = np.polyfit(x, y, deg)
f = np.poly1d(z)
# calculate new x's and y's
x_new = np.linspace(np.amin(x),... | {
"repo_name": "sujithvm/red-alert",
"path": "src/IPP_SNIP_parse.py",
"copies": "3",
"size": "5729",
"license": "mit",
"hash": 797139393441370000,
"line_mean": 30.1358695652,
"line_max": 190,
"alpha_frac": 0.6744632571,
"autogenerated": false,
"ratio": 2.6796071094480824,
"config_test": false,
... |
__author__ = 'sukrit'
from pkg_resources import resource_string
BUNDLED_TEMPLATE_PREFIX="bundled://"
RAW_TEMPLATE_PREFIX="raw://"
def fetch_template(template_url):
if template_url.startswith('http://') or \
template_url.startswith('https://'):
pass
if template_url.startswith(BUNDLED_TE... | {
"repo_name": "sukrit007/fleet-scheduler",
"path": "fleet/deploy/template_manager.py",
"copies": "1",
"size": "1036",
"license": "mit",
"hash": -1698456624298326500,
"line_mean": 32.4193548387,
"line_max": 78,
"alpha_frac": 0.6370656371,
"autogenerated": false,
"ratio": 3.767272727272727,
"conf... |
__author__ = 'sukrit'
import os
import SimpleHTTPServer
import SocketServer
import etcd
import requests
from threading import Thread
ETCD_PROXY_BASE = os.environ.get('ETCD_PROXY_BASE', '/yoda')
ETCD_HOST = os.environ.get('ETCD_HOST', 'localhost')
ETCD_PORT = int(os.environ.get('ETCD_PORT', '4001'))
MOCK_TCP_PORT = i... | {
"repo_name": "totem/yoda-proxy",
"path": "test/integration/__init__.py",
"copies": "1",
"size": "5534",
"license": "mit",
"hash": 2873847685220860000,
"line_mean": 32.5393939394,
"line_max": 79,
"alpha_frac": 0.6183592338,
"autogenerated": false,
"ratio": 3.435133457479826,
"config_test": fals... |
__author__ = 'sukrit'
class Provider:
"""
Base Provider class for API Client implementation.
"""
def __init__(self, **kwargs):
super(Provider, self).__init__()
def not_supported(self):
"""
Raises NotImplementedError with a message
:return:
"""
rais... | {
"repo_name": "totem/fleet-py",
"path": "fleet/client/fleet_base.py",
"copies": "1",
"size": "3221",
"license": "mit",
"hash": 5030226559908543000,
"line_mean": 32.206185567,
"line_max": 79,
"alpha_frac": 0.6010555728,
"autogenerated": false,
"ratio": 4.641210374639769,
"config_test": false,
... |
__author__ = 'sulantha'
from datetime import datetime
import itertools
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
count = 0
with open('/data/data02/sulantha/Marina_Sep_2016/Marina_2016Sep_Full_SQL_CSV_Bef_FDG_Scans.csv', 'r') as file:
next(file)
for line in file:
... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/marina/Marina_2016Sep_GetFDG.py",
"copies": "1",
"size": "1795",
"license": "apache-2.0",
"hash": -2308724912516439000,
"line_mean": 41.7619047619,
"line_max": 146,
"alpha_frac": 0.5732590529,
"autogenerated": false,
"ratio": 3.06837... |
__author__ = 'sulantha'
from Utils.DbUtils import DbUtils
DBClient = DbUtils()
RIDList = ['4225','4746','4799','4136','4142','4192','4713','4960','4387','0021','4827','4579','4580','4616','4668','4696','4809','4549','4680','5012','5019','4674','4757','4385','4721','4947','4714','4715','4736','4706','4720','4661','4728'... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/andrea/RemoveEntriesFromDBTables.py",
"copies": "1",
"size": "1231",
"license": "apache-2.0",
"hash": -8238911720778759000,
"line_mean": 71.4117647059,
"line_max": 424,
"alpha_frac": 0.6425670187,
"autogenerated": false,
"ratio": 2.6... |
__author__ = 'sulantha'
from Utils.DbUtils import DbUtils
from Manager.SQL.SQLBuilder import SQLBuilder
from Config import StudyConfig as sc
from Manager.SQLTables.ConversionObject import ConversionObject
class Conversion:
def __init__(self):
self.tableName = 'Conversion'
self.DBClient = DbUtils()
... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/SQLTables/Conversion.py",
"copies": "1",
"size": "3350",
"license": "apache-2.0",
"hash": 6267866664815984000,
"line_mean": 52.1746031746,
"line_max": 233,
"alpha_frac": 0.672238806,
"autogenerated": false,
"ratio": 3.936545240893... |
__author__ = 'sulantha'
from Utils.DbUtils import DbUtils
from Manager.SQL.SQLBuilder import SQLBuilder
from Config import StudyConfig as sc
from Manager.SQLTables.SortingObject import SortingObject
class Sorting:
def __init__(self):
self.tableName = 'Sorting'
self.DBClient = DbUtils()
self... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/SQLTables/Sorting.py",
"copies": "1",
"size": "1537",
"license": "apache-2.0",
"hash": 5467262065571800000,
"line_mean": 41.6944444444,
"line_max": 141,
"alpha_frac": 0.6746909564,
"autogenerated": false,
"ratio": 3.625,
"config... |
__author__ = 'sulantha'
from Utils.DbUtils import DbUtils
from Manager.SQL.SQLBuilder import SQLBuilder
from Manager.SQLTables.ProcessingObject import ProcessingObject
from Config import StudyConfig as sc
class Processing:
def __init__(self):
self.DBClient = DbUtils()
self.sqlBuilder = SQLBuilder(... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/SQLTables/Processing.py",
"copies": "1",
"size": "1676",
"license": "apache-2.0",
"hash": 5285752274115201000,
"line_mean": 48.2941176471,
"line_max": 139,
"alpha_frac": 0.6915274463,
"autogenerated": false,
"ratio": 3.82648401826... |
__author__ = 'Sulantha'
from Utils.DbUtils import DbUtils
from Utils.PipelineLogger import PipelineLogger
from Coregistration.CoregHandler import CoregHandler
from pymongo import MongoClient
import os, subprocess, difflib
import Config.PipelineConfig as pc
class PETHelper:
def __init__(self):
self.DBClient... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Pipelines/Helpers/PETHelper.py",
"copies": "1",
"size": "11524",
"license": "apache-2.0",
"hash": -8494376012580870000,
"line_mean": 54.4038461538,
"line_max": 287,
"alpha_frac": 0.5713293995,
"autogenerated": false,
"ratio": 3.6829658037... |
__author__ = 'Sulantha'
from Utils.DbUtils import DbUtils
class QCHandler:
def __init__(self):
self.DBClient = DbUtils()
def requestQC(self, study, modal_table, modal_tableId, qcField, qctype, qcFolder):
qcsql = "INSERT IGNORE INTO QC VALUES (Null, '{0}', '{1}', '{2}', '{3}', '{4}','{5}' , 0, ... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "QC/QCHandler.py",
"copies": "1",
"size": "1334",
"license": "apache-2.0",
"hash": 8561356315229255000,
"line_mean": 50.3076923077,
"line_max": 148,
"alpha_frac": 0.4385307346,
"autogenerated": false,
"ratio": 4.359477124183006,
"config_... |
__author__ = 'sulantha'
from Utils.PipelineLogger import PipelineLogger
from Utils.DbUtils import DbUtils
from QC.QCHandler import QCHandler
class QSubJobStatusReporter:
def __init__(self):
self.DBClient = DbUtils()
self.QCHandler = QCHandler()
def setStatus(self, job, status):
if job.... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/QSubJobStatusReporter.py",
"copies": "1",
"size": "5660",
"license": "apache-2.0",
"hash": -946863805116678400,
"line_mean": 61.9,
"line_max": 206,
"alpha_frac": 0.5568904594,
"autogenerated": false,
"ratio": 3.731048121292024,
... |
__author__ = 'sulantha'
import datetime
from Utils.DbUtils import DbUtils
csvFile = '/data/data03/sulantha/Downloads/av45_list.csv'
MatchDBClient = DbUtils(database='Study_Data.ADNI')
DBClient = DbUtils()
with open(csvFile, 'r') as csv:
next(csv)
for line in csv:
row = line.split(',')
rid = row[... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/getSystemVisitCode.py",
"copies": "1",
"size": "1186",
"license": "apache-2.0",
"hash": -6321006153446962000,
"line_mean": 39.8965517241,
"line_max": 185,
"alpha_frac": 0.5927487352,
"autogenerated": false,
"ratio": 3.196765498652291... |
__author__ = 'sulantha'
import datetime
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
with open('/data/data02/sulantha/Tharick_VBM/Av45_Date_and_RID', 'r') as file:
next(file)
for line in file:
row = line.split(',')
rid = row[0]
date = row[1].strip()
... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/andrea/getAndreaProcessedFilesAV45.py",
"copies": "1",
"size": "2420",
"license": "apache-2.0",
"hash": 8321829815800845000,
"line_mean": 38.6721311475,
"line_max": 151,
"alpha_frac": 0.5227272727,
"autogenerated": false,
"ratio": 2.... |
__author__ = 'sulantha'
import datetime
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
with open('/data/data03/sulantha/Downloads/fdg_list.csv', 'r') as file:
next(file)
for line in file:
row = line.split(',')
rid = row[0]
date = row[1].strip()
d... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/andrea/getAndreaProcessedFilesFDG.py",
"copies": "1",
"size": "2367",
"license": "apache-2.0",
"hash": 409729848985346370,
"line_mean": 37.8032786885,
"line_max": 150,
"alpha_frac": 0.5162653147,
"autogenerated": false,
"ratio": 3.02... |
__author__ = 'sulantha'
import datetime
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
with open('/data/data03/sulantha/MarinaAnalysis/AV45_list_with_dates.csv', 'r') as file:
next(file)
for line in file:
row = line.split(',')
rid = row[0]
date = row[1].... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/marina/getMarinaProcessedFilesAV45.py",
"copies": "1",
"size": "2437",
"license": "apache-2.0",
"hash": 1769159766905401600,
"line_mean": 38.9508196721,
"line_max": 151,
"alpha_frac": 0.5264669676,
"autogenerated": false,
"ratio": 3.... |
__author__ = 'sulantha'
import datetime
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
with open('/data/data03/sulantha/MarinaAnalysis/FDG_list_with_dates.csv', 'r') as file:
next(file)
for line in file:
row = line.split(',')
rid = row[0]
date = row[1].s... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/marina/getMarinaProcessedFilesFDG.py",
"copies": "1",
"size": "2385",
"license": "apache-2.0",
"hash": 8897382009742381000,
"line_mean": 38.0983606557,
"line_max": 150,
"alpha_frac": 0.5194968553,
"autogenerated": false,
"ratio": 3.0... |
__author__ = 'sulantha'
import glob, subprocess, re
from Utils.DbUtils import DbUtils
import os
from distutils import file_util, dir_util
import shutil
DBClient = DbUtils()
IID_list = ['45WL3UA1MPRAGEv0020111115xDICOM']
for iid in IID_list:
getDataFolderSQL = "SELECT RAW_FOLDER FROM Sorting WHERE I_IDENTIFIER = '{... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Utils/PurgeDataByIID.py",
"copies": "1",
"size": "2552",
"license": "apache-2.0",
"hash": -1593495921361936100,
"line_mean": 37.1044776119,
"line_max": 127,
"alpha_frac": 0.6504702194,
"autogenerated": false,
"ratio": 3.0673076923076925,
... |
__author__ = 'sulantha'
import glob, subprocess, re
from Utils.DbUtils import DbUtils
import os
from distutils import file_util, dir_util
import shutil
DBClient = DbUtils()
def recurseBeastFolder():
fileList = []
for name in glob.glob('/data/data03/ADNI/BEAST/adni_*/t1/beast/*'):
mainFolder = name
... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Utils/addOldBeastFiles.py",
"copies": "1",
"size": "5075",
"license": "apache-2.0",
"hash": 5282620139240241000,
"line_mean": 41.6470588235,
"line_max": 268,
"alpha_frac": 0.5972413793,
"autogenerated": false,
"ratio": 3.2160963244613434,... |
__author__ = 'sulantha'
import glob, subprocess, re
from Utils.DbUtils import DbUtils
import os
from distutils import file_util, dir_util
import shutil
DBClient = DbUtils()
getAllTodoSQL = "SELECT XFM_NAME FROM Coregistration WHERE END = 0 AND SKIP = 0 AND START = 0 AND PET_SCANTYPE = 'AV45'"
res = DBClient.executeAl... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Utils/FixRedoingCoregs.py",
"copies": "1",
"size": "1241",
"license": "apache-2.0",
"hash": -6220745232104206000,
"line_mean": 30.05,
"line_max": 120,
"alpha_frac": 0.64544722,
"autogenerated": false,
"ratio": 2.872685185185185,
"config... |
__author__ = 'Sulantha'
import math
from matplotlib import pyplot as plt
from sklearn.metrics import auc
from Python.RUSRandomForest import Config
import numpy
def getOptimalOparatingPoint(fpr, tpr, th):
distanceList = numpy.sqrt(numpy.power(fpr, 2) + numpy.power(tpr - 1, 2))
minIdx = numpy.argmin(distanceLis... | {
"repo_name": "sulantha2006/Conversion",
"path": "Python/RUSRandomForest/plotROCFromFile.py",
"copies": "1",
"size": "2544",
"license": "mit",
"hash": -8426556223427367000,
"line_mean": 47.9230769231,
"line_max": 119,
"alpha_frac": 0.6187106918,
"autogenerated": false,
"ratio": 2.8266666666666667... |
__author__ = 'Sulantha'
import numpy
from sklearn.ensemble import RandomForestClassifier
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
class RUSRandomForestClassifier:
def __init__(self, n_Forests=100, n_TreesInForest=200):
self.__n_Forests = n_Forests
self.__n... | {
"repo_name": "sulantha2006/Conversion",
"path": "Python/RUSRandomForest/RUSRandomForestClassifier.py",
"copies": "1",
"size": "4581",
"license": "mit",
"hash": 7355550064518408000,
"line_mean": 45.2727272727,
"line_max": 126,
"alpha_frac": 0.6347958961,
"autogenerated": false,
"ratio": 3.5319969... |
__author__ = 'Sulantha'
import numpy
import pandas as pd
from sklearn.linear_model import LogisticRegression
from sklearn import cross_validation
from sklearn.metrics import confusion_matrix
class RegularizedLogisticLearner:
def __init__(self):
pass
def trainLogisticRegreion(self):
pass
def m... | {
"repo_name": "sulantha2006/Conversion",
"path": "Python/RegularizedLogistic/runRegLogisticRegression.py",
"copies": "1",
"size": "1911",
"license": "mit",
"hash": -6465320527887154000,
"line_mean": 30.85,
"line_max": 103,
"alpha_frac": 0.6452119309,
"autogenerated": false,
"ratio": 2.90425531914... |
__author__ = 'sulantha'
import os
defaultT1config = "{'n3Dist':'75', 'headHeight':'150'}"
defaultAV45config = "{'blur':'8'}"
defaultAV1451config = "{'blur':'8'}"
defaultFDGconfig = "{'blur':'8'}"
defaultFMRIconfig = "{'nu_correct':'-75', 'fwhm_smoothing':'6'}"
# For Fmri
niak_location = '/data/data01/wang/references/... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Config/PipelineConfig.py",
"copies": "1",
"size": "5681",
"license": "apache-2.0",
"hash": -5859519372192091000,
"line_mean": 52.5943396226,
"line_max": 116,
"alpha_frac": 0.4536173209,
"autogenerated": false,
"ratio": 2.527135231316726,
... |
__author__ = 'sulantha'
import os
from Utils.DbUtils import DbUtils
import glob
DBClient = DbUtils()
outLines = []
count = 0
with open('/data/data02/sulantha/VBM_FDG/FDG_FULLPAT', 'r') as file:
for line in file:
row = line.split('/')
rid = row[6]
dirname = os.path.dirname(line)
na... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Test/getCIVETForPETFile.py",
"copies": "1",
"size": "1067",
"license": "apache-2.0",
"hash": -8315244488697230000,
"line_mean": 31.3636363636,
"line_max": 153,
"alpha_frac": 0.5838800375,
"autogenerated": false,
"ratio": 2.75,
"config_t... |
__author__ = 'Sulantha'
import pandas as pd
from matplotlib import pyplot as plt
from sklearn.metrics import roc_curve, auc, confusion_matrix
from Python.RUSRandomForest import RUSRandomForestClassifier
import pickle
mci_df = pd.read_csv('../../Classification_Table.csv', delimiter=',')
mci_df = mci_df.drop('ID', axis=... | {
"repo_name": "sulantha2006/Conversion",
"path": "Python/RUSRandomForest/runRUSRFC.py",
"copies": "1",
"size": "1610",
"license": "mit",
"hash": -8376649351377917000,
"line_mean": 35.6136363636,
"line_max": 132,
"alpha_frac": 0.7149068323,
"autogenerated": false,
"ratio": 2.6611570247933884,
"c... |
__author__ = 'sulantha'
import pandas as pd
import numpy
from matplotlib import pyplot as plt
from sklearn.metrics import roc_curve, auc, confusion_matrix
from Python.RUSRandomForest import RUSRandomForestClassifier
from Python.RUSRandomForest import Config
from multiprocessing import Pool
def writeSensAndSpec(fpr, t... | {
"repo_name": "sulantha2006/Conversion",
"path": "Python/RUSRandomForest/runClassificationHAI2016.py",
"copies": "1",
"size": "5995",
"license": "mit",
"hash": 8149228545522582000,
"line_mean": 46.2047244094,
"line_max": 175,
"alpha_frac": 0.6483736447,
"autogenerated": false,
"ratio": 3.09979317... |
__author__ = 'sulantha'
import subprocess
import os
import fnmatch
import distutils.dir_util
import distutils.file_util
import shutil
import glob
from Utils.PipelineLogger import PipelineLogger
class ADNI_V1_PET:
def __init__(self):
pass
def convert_nii(self, convertionObj):
rawFile = '{0}/*.... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Converters/ConversionScripts/ADNI_V1_PET.py",
"copies": "1",
"size": "18439",
"license": "apache-2.0",
"hash": -7222108393060974000,
"line_mean": 68.5811320755,
"line_max": 237,
"alpha_frac": 0.4706871305,
"autogenerated": false,
"ratio":... |
__author__ = 'sulantha'
import threading
import socket
from Utils.PipelineLogger import PipelineLogger
import datetime
from Manager.QSubJob import QSubJob
from Manager.QSubJobStatusReporter import QSubJobStatusReporter
class QSubJobHandler(threading.Thread):
submittedJobs = {'xxxx':QSubJob('xxxx', '23:59:59', None... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/QSubJobHanlder.py",
"copies": "1",
"size": "3930",
"license": "apache-2.0",
"hash": -7622021321602278000,
"line_mean": 45.7857142857,
"line_max": 153,
"alpha_frac": 0.5541984733,
"autogenerated": false,
"ratio": 4.051546391752577,... |
__author__ = 'Sulantha'
AllowedStudyList = ['ADNI', 'ADNI_OLD', 'DIAN']
AllowedStepsList = ['Sort', 'Move', 'T1Beast', 'T1Process', 'ProcessAV45', 'ProcessFDG', 'ProcessFMRI', 'ProcessDTI', 'ProcessAV1451']
AllowedVersions = ['V1', 'V2', 'V3']
AllowedModalityList = ['T1', 'AV45', 'FDG', 'FMRI', 'BLUFF', 'AV1451', 'PIB... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Config/StudyConfig.py",
"copies": "1",
"size": "5428",
"license": "apache-2.0",
"hash": 1051732403233809900,
"line_mean": 57.3655913978,
"line_max": 134,
"alpha_frac": 0.2584745763,
"autogenerated": false,
"ratio": 4.197989172467131,
"c... |
__author__ = 'sulantha'
class ConversionObject:
def __init__(self, values):
self.record_id = 0 if 'record_id' not in values else values['record_id']
self.study = values['study']
self.rid = values['rid']
self.scan_type = values['scan_type']
self.scan_date = values['scan_date'... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/SQLTables/ConversionObject.py",
"copies": "1",
"size": "1693",
"license": "apache-2.0",
"hash": 3094335593315558000,
"line_mean": 44.7837837838,
"line_max": 107,
"alpha_frac": 0.5451860602,
"autogenerated": false,
"ratio": 3.66450... |
__author__ = 'sulantha'
class ProcessingObject:
def __init__(self, values):
self.record_id = 0 if 'record_id' not in values else values['record_id']
self.study = values['study']
self.rid = values['rid']
self.modality = values['modality']
self.scan_date = values['scan_date']
... | {
"repo_name": "sulantha2006/Processing_Pipeline",
"path": "Manager/SQLTables/ProcessingObject.py",
"copies": "1",
"size": "1463",
"license": "apache-2.0",
"hash": 4677492442566853000,
"line_mean": 44.71875,
"line_max": 112,
"alpha_frac": 0.5543403964,
"autogenerated": false,
"ratio": 3.6212871287... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.