text stringlengths 0 1.05M | meta dict |
|---|---|
__author__ = 'admin'
class Contact:
def __init__(self,
firstname=None,
middlename=None,
lastname=None,
nickname=None,
title=None,
company=None,
address=None,
phone_home=None,
... | {
"repo_name": "dimchenkoAlexey/python_training",
"path": "model/contact.py",
"copies": "1",
"size": "1888",
"license": "apache-2.0",
"hash": -62076236253906664,
"line_mean": 33.3272727273,
"line_max": 70,
"alpha_frac": 0.5296610169,
"autogenerated": false,
"ratio": 4.1677704194260485,
"config_t... |
__author__ = 'admin'
class GroupHelper:
def __init__(self, app):
self.app = app
def open_groups_page(self):
wd = self.app.wd
if not (wd.current_url.endswith("groups.php") and len(wd.find_elements_by_name("new")) > 0 ):
# open groups page
wd.find_element_by_link... | {
"repo_name": "dimchenkoAlexey/python_training",
"path": "fixture/group.py",
"copies": "1",
"size": "2151",
"license": "apache-2.0",
"hash": 4080216671805314000,
"line_mean": 31.1044776119,
"line_max": 101,
"alpha_frac": 0.5788005579,
"autogenerated": false,
"ratio": 3.5262295081967214,
"config... |
__author__ = 'admin'
from tastypie.resources import ModelResource, ALL, ALL_WITH_RELATIONS
from ResumeViewer.models import Job
from tastypie.authentication import SessionAuthentication
from tastypie.authorization import Authorization
from django.contrib.auth.models import User
from tastypie import fields
from Re... | {
"repo_name": "fawazn/Resume-Viewer",
"path": "ResumeViewer/api.py",
"copies": "1",
"size": "1230",
"license": "mit",
"hash": 4851212037286071000,
"line_mean": 35.2727272727,
"line_max": 84,
"alpha_frac": 0.6723577236,
"autogenerated": false,
"ratio": 4.315789473684211,
"config_test": false,
... |
__author__ = 'admiral0'
import os.path as path
from os import walk
from .Common import read_json, mod_file_name
import re
from .Exceptions import JsonNotValid, RepositoryDirectoryDoesNotExist, RepositoryDoesNotHaveMetaJson, ModDoesNotExistInRepo
from .Mod import Mod
class ModRepository:
_metaname = 'meta.json'
... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/ModRepository.py",
"copies": "1",
"size": "2121",
"license": "bsd-2-clause",
"hash": -7735896267788268000,
"line_mean": 34.35,
"line_max": 123,
"alpha_frac": 0.5799151344,
"autogenerated": false,
"ratio": 4.00945179584121,
"config_test"... |
__author__ = 'admiral0'
import os.path as path
import re
from .Exceptions import JsonNotValid, ModDoesNotExist, ModJsonDoesNotExist, ModVersionDoesNotExistInRepo
from .Common import *
def validate_version(ver):
assert type(ver) is str
if not re.match(r'^[a-zA-Z_0-9\.\-()]+$', ver):
return ['Version ' ... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/Mod.py",
"copies": "1",
"size": "3351",
"license": "bsd-2-clause",
"hash": -4919725087683980000,
"line_mean": 32.8484848485,
"line_max": 117,
"alpha_frac": 0.5455088033,
"autogenerated": false,
"ratio": 3.6864686468646863,
"config_test"... |
__author__ = 'admiral0'
from .Exceptions import JsonNotValid as JsonError
import json
mod_file_name = 'mod.json'
minecraft_version_regex = r'^\d+\.\d+(\.\d+)?$'
url_regex = r'^https?:.*$'
def validate(entities, json, obj):
assert type(entities) is dict
assert type(json) is dict
errors = []
for key ... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/Common.py",
"copies": "1",
"size": "1115",
"license": "bsd-2-clause",
"hash": 8982802856973529000,
"line_mean": 28.3684210526,
"line_max": 101,
"alpha_frac": 0.5264573991,
"autogenerated": false,
"ratio": 3.8054607508532423,
"config_tes... |
__author__ = 'admiral0'
from . import *
from .Exceptions import JsonNotValid
import argparse
import os.path as path
def is_mod_repo(x):
if path.isdir(x):
return x
raise argparse.ArgumentTypeError(x + ' is not a Directory')
def validate(args):
try:
repo = ModRepository(args.mod_repo)
... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/Util.py",
"copies": "1",
"size": "1509",
"license": "bsd-2-clause",
"hash": -669733687798787700,
"line_mean": 24.593220339,
"line_max": 108,
"alpha_frac": 0.6037110669,
"autogenerated": false,
"ratio": 3.5011600928074245,
"config_test":... |
__author__ = 'admiral0'
from os import path
import re
from .Exceptions import RepositoryDirectoryDoesNotExist
from .Exceptions import JsonNotValid
from .Exceptions import RepositoryDoesNotHaveMetaJson
from .Exceptions import ModDoesNotExistInRepo, ModVersionDoesNotExistInRepo
from .Exceptions import BranchDoesNotExist... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/PackRepository.py",
"copies": "1",
"size": "3328",
"license": "bsd-2-clause",
"hash": -3360804042245640700,
"line_mean": 33.6666666667,
"line_max": 118,
"alpha_frac": 0.5522836538,
"autogenerated": false,
"ratio": 3.9199057714958774,
"c... |
__author__ = 'admiral0'
class RepositoryDirectoryDoesNotExist(Exception):
def __init__(self, path):
self.path = path
def __str__(self):
return 'The mod repository does not exist. Missing directory:' + self.path
class RepositoryDoesNotHaveMetaJson(Exception):
def __init__(self, path):
... | {
"repo_name": "admiral0/AntaniRepos",
"path": "antanirepos/Exceptions.py",
"copies": "1",
"size": "2222",
"license": "bsd-2-clause",
"hash": 168683478481213950,
"line_mean": 23.4285714286,
"line_max": 104,
"alpha_frac": 0.5814581458,
"autogenerated": false,
"ratio": 3.8442906574394464,
"config_... |
__author__ = 'Adnan Siddiqi<kadnanATgmail.com>'
import os
import json
def get_json(jsondata):
json_object = None
try:
json_object = json.loads(jsondata)
except ValueError, e:
return None
return json_object
def generate_manifest_text(json_dict):
content = ''
content = '{\n'
... | {
"repo_name": "kadnan/extGen",
"path": "extgen.py",
"copies": "1",
"size": "3399",
"license": "mit",
"hash": 8086151418734336000,
"line_mean": 32.6534653465,
"line_max": 142,
"alpha_frac": 0.496616652,
"autogenerated": false,
"ratio": 4.175675675675675,
"config_test": false,
"has_no_keywords"... |
__author__ = 'ad'
from abc import ABCMeta
from collections import OrderedDict
import importhelpers
class BaseField(object):
__metaclass__ = ABCMeta
def __init__(self, required=False, field_name=None):
super(BaseField, self).__init__()
self.required = required
self.field_name = field... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/pyraml/fields.py",
"copies": "1",
"size": "15212",
"license": "mit",
"hash": 4171094303208123400,
"line_mean": 26.3615107914,
"line_max": 121,
"alpha_frac": 0.5474625296,
"autogenerated": false,
"ratio": 4.482027106658809... |
__author__ = 'ad'
from collections import OrderedDict
from pyapi.libraries.pyraml_parser_master.pyraml.model import Model
from pyapi.libraries.pyraml_parser_master.pyraml.fields import List, String, Reference, Map, Or, Float
def test_model_structure_inheritance():
class Thing(Model):
inner = List(String... | {
"repo_name": "mpetyx/pyapi",
"path": "tests/unit/raml/tests/test_model.py",
"copies": "1",
"size": "2243",
"license": "mit",
"hash": -1278097456890495000,
"line_mean": 25.4,
"line_max": 102,
"alpha_frac": 0.6495764601,
"autogenerated": false,
"ratio": 3.5101721439749607,
"config_test": true,
... |
__author__ = 'ad'
from fields import BaseField, String, List
class ValidationError(StandardError):
def __init__(self, validation_errors):
self.errors = validation_errors
def __repr__(self):
return u"ValidationError: " + repr(self.errors)
class BaseModel(object):
pass
class Schema(typ... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/pyraml/model.py",
"copies": "1",
"size": "4247",
"license": "mit",
"hash": -5772385761777201000,
"line_mean": 32.7063492063,
"line_max": 101,
"alpha_frac": 0.5669884624,
"autogenerated": false,
"ratio": 4.255511022044089,... |
__author__ = 'ad'
from model import Model
from fields import String, Reference, Map, List, Bool, Int, Float, Or
class RamlDocumentation(Model):
content = String()
title = String()
class RamlSchema(Model):
name = String()
type = String()
schema = String()
example = String()
class RamlQuery... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/pyraml/entities.py",
"copies": "1",
"size": "3805",
"license": "mit",
"hash": -8072963671755983000,
"line_mean": 25.4305555556,
"line_max": 106,
"alpha_frac": 0.5802890933,
"autogenerated": false,
"ratio": 4.2466517857142... |
__author__ = 'ad'
import contextlib
import urllib2
import mimetypes
import os.path
import urlparse
from collections import OrderedDict
import yaml
from raml_elements import ParserRamlInclude
from fields import String, Reference
from entities import RamlRoot, RamlResource, RamlMethod, RamlBody, RamlResourceType, Raml... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/pyraml/parser.py",
"copies": "1",
"size": "17263",
"license": "mit",
"hash": 3258387050526762000,
"line_mean": 31.5103578154,
"line_max": 120,
"alpha_frac": 0.6459479812,
"autogenerated": false,
"ratio": 3.903007008817544... |
__author__ = 'ad'
import os.path
from collections import OrderedDict
from pyapi.libraries.pyraml_parser_master import pyraml
from pyapi.libraries.pyraml_parser_master.pyraml import parser
from pyapi.libraries.pyraml_parser_master.pyraml.entities import RamlResource, RamlMethod, RamlQueryParameter
fixtures_dir = os.... | {
"repo_name": "mpetyx/pyapi",
"path": "tests/unit/raml/tests/test_resources.py",
"copies": "1",
"size": "5323",
"license": "mit",
"hash": 5145549093047658000,
"line_mean": 46.954954955,
"line_max": 109,
"alpha_frac": 0.7422506106,
"autogenerated": false,
"ratio": 3.8544532947139754,
"config_tes... |
__author__ = 'ad'
import os.path
from pyapi.libraries.pyraml_parser_master import pyraml
from pyapi.libraries.pyraml_parser_master.pyraml import parser
from pyapi.libraries.pyraml_parser_master.pyraml.entities import RamlRoot, RamlDocumentation
fixtures_dir = os.path.join(os.path.dirname(__file__), '../', 'samples'... | {
"repo_name": "mpetyx/pyapi",
"path": "tests/unit/raml/tests/test_documentation.py",
"copies": "1",
"size": "1551",
"license": "mit",
"hash": 5138264116498412000,
"line_mean": 39.8157894737,
"line_max": 93,
"alpha_frac": 0.7137330754,
"autogenerated": false,
"ratio": 3.3426724137931036,
"config... |
__author__ = 'ad'
import os.path
import pyraml.parser
from pyraml.entities import RamlRoot, RamlDocumentation
fixtures_dir = os.path.join(os.path.dirname(__file__), '..', 'samples')
def test_include_raml():
p = pyraml.parser.load(os.path.join(fixtures_dir, 'root-elements-includes.yaml'))
assert isinstance... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/tests/test_documentation.py",
"copies": "1",
"size": "1400",
"license": "mit",
"hash": 3066141277163579000,
"line_mean": 36.8378378378,
"line_max": 86,
"alpha_frac": 0.7021428571,
"autogenerated": false,
"ratio": 3.341288... |
__author__ = 'ad'
import os.path
import pyraml.parser
from pyraml.entities import RamlRoot, RamlTrait, RamlBody, RamlResourceType
fixtures_dir = os.path.join(os.path.dirname(__file__), '..', 'samples')
def test_parse_traits_with_schema():
p = pyraml.parser.load(os.path.join(fixtures_dir, 'media-type.yaml'))
... | {
"repo_name": "mpetyx/pyapi",
"path": "pyapi/libraries/pyraml_parser_master/tests/test_traits.py",
"copies": "1",
"size": "2161",
"license": "mit",
"hash": -8278884886732771000,
"line_mean": 39.037037037,
"line_max": 105,
"alpha_frac": 0.6968995835,
"autogenerated": false,
"ratio": 3.309341500765... |
import os.path as op
import numpy as np
import pytest
from numpy.testing import assert_allclose
from mne.chpi import read_head_pos
from mne.datasets import testing
from mne.io import read_raw_fif
from mne.preprocessing import (annotate_movement, compute_average_dev_head_t,
annotate_musc... | {
"repo_name": "bloyl/mne-python",
"path": "mne/preprocessing/tests/test_artifact_detection.py",
"copies": "3",
"size": "7531",
"license": "bsd-3-clause",
"hash": 1854888127119699500,
"line_mean": 34.0279069767,
"line_max": 79,
"alpha_frac": 0.6154561147,
"autogenerated": false,
"ratio": 3.4403837... |
__author__ = "Adrian 'LucidCharts' Campos, Johnson Nguyen, Josh Hicken"
from string import punctuation as punc_chars # this is a string of punctuation chars from python standard lib
from collections import OrderedDict
# noinspection SpellCheckingInspection
ALPHABET = 'abcdefghijklmnopqrstuvwxyz'
def get_frequency_di... | {
"repo_name": "adriancampos/LetsPlotMoby",
"path": "dist_calculators.py",
"copies": "1",
"size": "3086",
"license": "mit",
"hash": 2544198012438133000,
"line_mean": 40.1466666667,
"line_max": 112,
"alpha_frac": 0.6824368114,
"autogenerated": false,
"ratio": 4.136729222520107,
"config_test": fal... |
__author__ = 'adrianmo'
import string
import re
import codecs,sys, unicodedata
import pprint
import MySQLdb
import os
xuser = "root"
xpasswd = "cRe33Eth"
xhost = '127.0.0.1'
xport = 3334
class MMICProgram():
def openFile(self, fileName):
with codecs.open (fileName, "r", "utf-8") as line:
... | {
"repo_name": "maplechori/pyblasv3",
"path": "MMICProgram.py",
"copies": "1",
"size": "1173",
"license": "mit",
"hash": 5552391818719018000,
"line_mean": 16.5074626866,
"line_max": 99,
"alpha_frac": 0.5549872123,
"autogenerated": false,
"ratio": 3.3901734104046244,
"config_test": false,
"has_... |
__author__ = 'adrian'
from PyQt4 import QtGui
from parking_app.UI.PlatformUI import PlatformUI
import parking_app.Common as Common
class CylinderUI(QtGui.QWidget):
def __init__(self, cylinder):
super(CylinderUI, self).__init__()
self.cylinder = cylinder
self.init_ui()
def init_ui(se... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/UI/CylinderUI.py",
"copies": "1",
"size": "1177",
"license": "mit",
"hash": 4083419530887316500,
"line_mean": 30,
"line_max": 87,
"alpha_frac": 0.5972812234,
"autogenerated": false,
"ratio": 3.7129337539432177,
"config_test... |
__author__ = 'adrian'
from PyQt4 import QtGui
from PyQt4 import QtCore
import parking_app.Common as Common
import random
class WithdrawFormUI(QtGui.QWidget):
# level, column, vehicle id, vehicle weight
update = QtCore.pyqtSignal(int, str, int)
def __init__(self, parking_slot, parent=None):
supe... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/UI/WithdrawFormUI.py",
"copies": "1",
"size": "1733",
"license": "mit",
"hash": -7218549468163006000,
"line_mean": 27.4098360656,
"line_max": 65,
"alpha_frac": 0.64050779,
"autogenerated": false,
"ratio": 3.617954070981211,
... |
__author__ = 'adrian'
from PyQt4 import QtGui
from PyQt4 import QtCore
from parking_app.UI.WarningConfirmationUI import WarningConfirmationUI
import parking_app.Common as Common
import random
class PlatformUI(QtGui.QWidget):
def __init__(self):
super(PlatformUI, self).__init__()
self.initUI()
... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/UI/PlatformUI.py",
"copies": "1",
"size": "4814",
"license": "mit",
"hash": 31891601509830390,
"line_mean": 31.7551020408,
"line_max": 109,
"alpha_frac": 0.6186123806,
"autogenerated": false,
"ratio": 3.910641754670999,
"co... |
__author__ = 'adrian'
from PyQt4 import QtGui
from PyQt4 import QtCore
import random
class WarningConfirmationUI(QtGui.QWidget):
stopAlarm = QtCore.pyqtSignal()
def __init__(self,parent=None):
super(WarningConfirmationUI, self).__init__(parent)
self.initUI()
def initUI(self):
... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/UI/WarningConfirmationUI.py",
"copies": "1",
"size": "1515",
"license": "mit",
"hash": 763752386227031800,
"line_mean": 25.1206896552,
"line_max": 85,
"alpha_frac": 0.6501650165,
"autogenerated": false,
"ratio": 3.97637795275... |
__author__ = 'adrian'
from PyQt4 import QtGui
import parking_app.Common as Common
from multiprocessing import Queue
class CarFormUI(QtGui.QWidget):
def __init__(self, input_queue):
super(CarFormUI, self).__init__()
self.__input_queue = input_queue
self.initUI()
def initUI(self):
... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/UI/CarFormUI.py",
"copies": "1",
"size": "3836",
"license": "mit",
"hash": -8870239060691166000,
"line_mean": 31.7863247863,
"line_max": 78,
"alpha_frac": 0.6470281543,
"autogenerated": false,
"ratio": 3.7644749754661433,
"... |
__author__ = 'adrian'
import sys
from PyQt4 import QtGui
from PyQt4 import QtCore
import time
from parking_app.UI.CylinderUI import CylinderUI
from parking_app.UI.CarFormUI import CarFormUI
from parking_app.UI.ParkingSlotsUI import ParkingSlotsUI
from parking_app.UI.WithdrawFormUI import WithdrawFormUI
import park... | {
"repo_name": "Nebla/cylindricalParkingPrototype",
"path": "parking_app/application.py",
"copies": "1",
"size": "9061",
"license": "mit",
"hash": 5700877106391525000,
"line_mean": 36.601659751,
"line_max": 125,
"alpha_frac": 0.6618474782,
"autogenerated": false,
"ratio": 3.514740108611327,
"con... |
__author__ = 'adria'
#!/usr/bin/python
from dataBase import *
import sys
sys.path.insert(0, '../model') #sino no deixa importar...
from owner import *
class UserLogin:
def __init__(self, owner):
self.owner = owner
self.db = DataBase()
self.registered = False #si l'usuari ja ha fet loguin ... | {
"repo_name": "aramusss/contableplus",
"path": "controller/userLogin.py",
"copies": "1",
"size": "4041",
"license": "apache-2.0",
"hash": 7223837244720342000,
"line_mean": 32.6833333333,
"line_max": 98,
"alpha_frac": 0.5288294976,
"autogenerated": false,
"ratio": 4.024900398406374,
"config_test... |
__author__ = 'adria'
#!/usr/bin/python
import os.path, random
class DataBase:
def __init__(self, rutaUsers="../database/usuaris.txt", rutaComptes="../database/comptes.txt"):
self.rutaUsers = rutaUsers
self.rutaComptes = rutaComptes
#User management methods:
def creaUsers(self):
"... | {
"repo_name": "aramusss/contableplus",
"path": "controller/dataBase.py",
"copies": "1",
"size": "8241",
"license": "apache-2.0",
"hash": 4140672061878445600,
"line_mean": 34.9781659389,
"line_max": 119,
"alpha_frac": 0.5162034227,
"autogenerated": false,
"ratio": 3.9084440227703983,
"config_tes... |
# import the necessary packages
import numpy as np
class Searcher:
def __init__(self, index):
# store our index of images
self.index = index
def search(self, queryFeatures):
# initialize our dictionary of results
results = {}
# loop over the index
for (k, features) in self.index.items():
# compute ... | {
"repo_name": "fffy2366/image-processing",
"path": "bin/python/pyimagesearch/searcher.py",
"copies": "3",
"size": "1417",
"license": "mit",
"hash": 7234219419886892000,
"line_mean": 30.5111111111,
"line_max": 61,
"alpha_frac": 0.691601976,
"autogenerated": false,
"ratio": 3.430992736077482,
"co... |
# USAGE
# python index.py --dataset images --index index.cpickle
# import the necessary packages
from pyimagesearch.rgbhistogram import RGBHistogram
import argparse
import cPickle
import glob
import cv2
# construct the argument parser and parse the arguments
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--da... | {
"repo_name": "fffy2366/image-processing",
"path": "bin/python/search_index.py",
"copies": "1",
"size": "1551",
"license": "mit",
"hash": -222828673452366000,
"line_mean": 30.04,
"line_max": 71,
"alpha_frac": 0.7272727273,
"autogenerated": false,
"ratio": 3.525,
"config_test": false,
"has_no_... |
# USAGE
# python search_external.py --dataset images --index index.cpickle --query queries/rivendell-query.png
# import the necessary packages
from pyimagesearch.rgbhistogram import RGBHistogram
from pyimagesearch.searcher import Searcher
import numpy as np
import argparse
import cPickle
import cv2
import time
from p... | {
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"path": "bin/python/search_external.py",
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# USAGE
# python search_index_one.py --dataset ../../public/uploads/similar --index ../../public/uploads/similar.cpickle --file 1464318452058AFC4E73.jpg
# import the necessary packages
from pyimagesearch.rgbhistogram import RGBHistogram
import argparse
import cPickle
import glob
import cv2
import os
import sys
import... | {
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# USAGE
# python search.py --dataset images --index index.cpickle
# import the necessary packages
from pyimagesearch.searcher import Searcher
import numpy as np
import argparse
import cPickle
import cv2
import time
from pyimagesearch import logger
conf = logger.Logger()
# construct the argument parser and parse the... | {
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"line_max": 86,
"alpha_frac": 0.6180811808,
"autogenerated": false,
"ratio": 3.465473145780051,
"config_test": fal... |
# import the necessary packages
from __future__ import print_function
import imutils
import cv2
# print the current OpenCV version on your system
print("Your OpenCV version: {}".format(cv2.__version__))
# check to see if you are using OpenCV 2.X
print("Are you using OpenCV 2.X? {}".format(imutils.is_cv2()))
# check... | {
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... |
# import the necessary packages
from scipy.spatial import distance as dist
import numpy as np
import cv2
def order_points(pts):
# sort the points based on their x-coordinates
xSorted = pts[np.argsort(pts[:, 0]), :]
# grab the left-most and right-most points from the sorted
# x-roodinate points
le... | {
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"line_max": 70,
"alpha_frac": 0.6427289048,
"autogenerated": false,
"ratio": 3.355421686746988,
"config_test": false,
... |
# import the necessary packages
import cv2
def sort_contours(cnts, method="left-to-right"):
# initialize the reverse flag and sort index
reverse = False
i = 0
# handle if we need to sort in reverse
if method == "right-to-left" or method == "bottom-to-top":
reverse = True
# handle if... | {
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"ha... |
# import the necessary packages
import numpy as np
import cv2
import sys
# import any special Python 2.7 packages
if sys.version_info.major == 2:
from urllib import urlopen
# import any special Python 3 packages
elif sys.version_info.major == 3:
from urllib.request import urlopen
def translate(image, x, y):... | {
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"path": "imutils/convenience.py",
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"autogenerated": false,
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... |
# import the necessary packages
import numpy as np
import cv2
def order_points(pts):
# initialize a list of coordinates that will be ordered
# such that the first entry in the list is the top-left,
# the second entry is the top-right, the third is the
# bottom-right, and the fourth is the bottom-left
... | {
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"path": "imutils/perspective.py",
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"line_max": 70,
"alpha_frac": 0.6405638215,
"autogenerated": false,
"ratio": 3.338562091503268,
"config_test": false,
"h... |
# import the necessary packages
import numpy as np
import urllib
import cv2
def translate(image, x, y):
# define the translation matrix and perform the translation
M = np.float32([[1, 0, x], [0, 1, y]])
shifted = cv2.warpAffine(image, M, (image.shape[1], image.shape[0]))
# return the translated image... | {
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"path": "imutils/convenience.py",
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"config_tes... |
# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python fps_demo.py
# python fps_demo.py --display 1
# import the necessary packages
from __future__ import print_function
from imutils.video import VideoStream
from imutils.video import FPS
import argparse
import imutils
import cv2
# construct ... | {
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"line_mean": 28.5975609756,
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"has... |
# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python image_basics.py
# import the necessary packages
import matplotlib.pyplot as plt
import imutils
import cv2
# load the example images
bridge = cv2.imread("../demo_images/bridge.jpg")
cactus = cv2.imread("../demo_images/cactus.jpg")
logo = ... | {
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"... |
# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python picamera_fps_demo.py
# python picamera_fps_demo.py --display 1
# import the necessary packages
from __future__ import print_function
from imutils.video import VideoStream
from imutils.video import FPS
from picamera.array import PiRGBArray... | {
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# USAGE
# BE SURE TO INSTALL 'imutils' PRIOR TO EXECUTING THIS COMMAND
# python sorting_contours.py
# import the necessary packages
from imutils import contours
import imutils
import cv2
# load the shapes image clone it, convert it to grayscale, and
# detect edges in the image
image = cv2.imread("../demo_images/shap... | {
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"path": "demos/sorting_contours.py",
"copies": "1",
"size": "1343",
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__author__ = "Adrian Soghoian & Omar Ahmad"
import subprocess
import reference
import models
"""
This subsystem contains functionality to scan the local network for connected
devices, their OS fingerprints, and any open ports that they may have.
"""
def scan_network(range, gateway="Unknown"):
"""
This m... | {
"repo_name": "adriansoghoian/security-at-home",
"path": "scanner.py",
"copies": "1",
"size": "3395",
"license": "mit",
"hash": -7437007821162580000,
"line_mean": 29.0442477876,
"line_max": 120,
"alpha_frac": 0.6159057437,
"autogenerated": false,
"ratio": 3.7144420131291027,
"config_test": fals... |
__author__ = 'Adrian Strilchuk'
from datetime import datetime, date
import json
def jsonify(obj):
return json.dumps(obj, ensure_ascii=False, separators=(u",", u":"))
# http://stackoverflow.com/questions/14163399/convert-list-of-datestrings-to-datetime-very-slow-with-python-strptime
def parse_datetime(dt_str):
... | {
"repo_name": "astrilchuk/sd2xmltv",
"path": "libschedulesdirect/__init__.py",
"copies": "1",
"size": "1338",
"license": "mit",
"hash": -5098791886143433000,
"line_mean": 24.7307692308,
"line_max": 117,
"alpha_frac": 0.620328849,
"autogenerated": false,
"ratio": 3.3118811881188117,
"config_test... |
__author__ = 'adrie_000'
# -*- coding: utf8 -*-
import numpy as np
class StrategicMind():
def __init__(self, data_center):
self.data_center = data_center
def set_objective(self):
best_obj = None
best_score = 0
for objective in self.data_center.objectives:
score = ... | {
"repo_name": "adrien-bellaiche/ia-cdf-rob-2015",
"path": "Strategy.py",
"copies": "1",
"size": "1044",
"license": "apache-2.0",
"hash": 9211860640672674000,
"line_mean": 29.7058823529,
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"alpha_frac": 0.5632183908,
"autogenerated": false,
"ratio": 4.261224489795918,
"config_test... |
__author__ = 'adrie_000'
import numpy as np
class Pathfinder():
def __init__(self, data_center):
self.data_center = data_center
def get_orders(self, objective):
# Renvoie les ordres en [direction, vitesse, vitesse_rotation]
objective_location = objective.position
v = np.array... | {
"repo_name": "adrien-bellaiche/ia-cdf-rob-2015",
"path": "Pathfinding.py",
"copies": "1",
"size": "1326",
"license": "apache-2.0",
"hash": 8245232157281335000,
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"autogenerated": false,
"ratio": 3.3400503778337534,
"config_... |
__author__ = "adrn <adrn@astro.columbia.edu>"
# Standard library
import os
import sys
import pickle
# Third-party
from astropy import log as logger
import astropy.coordinates as coord
import astropy.units as u
import emcee
import matplotlib.pyplot as pl
import numpy as np
import scipy.optimize as so
import h5py
impor... | {
"repo_name": "adrn/StreamBFE",
"path": "scripts/fit-streams.py",
"copies": "1",
"size": "15207",
"license": "mit",
"hash": -7465350424517582000,
"line_mean": 41.0082872928,
"line_max": 117,
"alpha_frac": 0.5885447491,
"autogenerated": false,
"ratio": 3.442055228610231,
"config_test": false,
... |
__author__ = "adrn <adrn@astro.columbia.edu>"
# Standard library
import os
# Third-party
from astropy.constants import G
from astropy import log as logger
from astropy.coordinates.angles import rotation_matrix
import astropy.coordinates as coord
import astropy.units as u
import matplotlib.pyplot as pl
import numpy as... | {
"repo_name": "adrn/StreamBFE",
"path": "scripts/make-streams.py",
"copies": "1",
"size": "8752",
"license": "mit",
"hash": 4981783500701987000,
"line_mean": 41.6926829268,
"line_max": 96,
"alpha_frac": 0.5631855576,
"autogenerated": false,
"ratio": 3.3700423565652677,
"config_test": false,
"... |
__author__ = "aemerick <emerick@astro.columbia.edu>"
class constants:
"""
Helpful contants. In cgs or cgs conversions
except ionization energies (eV)
"""
def __init__(self):
self.eV_erg = 6.24150934326E11
self.k_boltz = 1.380658E-16
self.c = 2.99792458E10
s... | {
"repo_name": "aemerick/onezone",
"path": "constants.py",
"copies": "1",
"size": "1446",
"license": "mit",
"hash": 3753205545507099000,
"line_mean": 28.5102040816,
"line_max": 94,
"alpha_frac": 0.5380359613,
"autogenerated": false,
"ratio": 2.6386861313868613,
"config_test": false,
"has_no_ke... |
__author__ = "aemerick <emerick@astro.columbia.edu>"
# --- external ---
from collections import OrderedDict
# --- internal ---
from constants import CONST as const
import imf as imf
#
# --------- Superclass for all parameters -------
#
class _parameters(object):
def __init__(self):
pass
def help(s... | {
"repo_name": "aemerick/onezone",
"path": "config.py",
"copies": "1",
"size": "12565",
"license": "mit",
"hash": 3984535296525362700,
"line_mean": 32.0657894737,
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"config_test": false,
"has_no_keyw... |
__author__ = "aemerick <emerick@astro.columbia.edu>"
# --- external ---
import numpy as np
# --- internal ---
from constants import CONST as const
# helper functions for computing physics models
def s99_wind_velocity(L, M, T, Z):
"""
Starburt99 stellar wind velocity model which computes
the stellar wind... | {
"repo_name": "aemerick/onezone",
"path": "physics.py",
"copies": "1",
"size": "4845",
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"config_test": false,
"has_no_keyw... |
__author__ = 'aerospike'
import copy
import ntpath
from lib import logutil
import os
from lib.logsnapshot import LogSnapshot
from lib.serverlog import ServerLog
from lib.logreader import LogReader, SHOW_RESULT_KEY, COUNT_RESULT_KEY, END_ROW_KEY, TOTAL_ROW_HEADER
from lib import terminal
import re
DT_FMT = "%b %d %Y %... | {
"repo_name": "tejassp/asadmn-web",
"path": "webapp/lib/logger.py",
"copies": "1",
"size": "27000",
"license": "unlicense",
"hash": 4269503029939395600,
"line_mean": 42.2692307692,
"line_max": 194,
"alpha_frac": 0.516,
"autogenerated": false,
"ratio": 4.184100418410042,
"config_test": false,
... |
__author__ = 'Afief'
from datetime import datetime
from peewee import CharField, TextField, BooleanField, ForeignKeyField, \
DateField
from apps.models import db
from apps.models.auth import User
class Phile(db.Model):
filename = CharField(max_length=100)
filetype = CharField(max_length=100)
filepa... | {
"repo_name": "ap13p/elearn",
"path": "apps/models/others.py",
"copies": "1",
"size": "1428",
"license": "bsd-3-clause",
"hash": 4063173094192638500,
"line_mean": 22.4098360656,
"line_max": 73,
"alpha_frac": 0.6862745098,
"autogenerated": false,
"ratio": 3.230769230769231,
"config_test": false,... |
__author__ = 'aftab'
import atom
import basisset
import molecule
import pertabdict
import shell
#Basis set parser for standard basis set files in Quantum Chemistry
#Tested as working on 2/3/2014 by Aftab Patel
#TODO: Add some safety
#utility function to count no of lines in a file
def file_len(file_reference):
p... | {
"repo_name": "stringtheorist/chem_parser",
"path": "parser.py",
"copies": "1",
"size": "3090",
"license": "mit",
"hash": -8491390544762819000,
"line_mean": 29.2941176471,
"line_max": 79,
"alpha_frac": 0.5598705502,
"autogenerated": false,
"ratio": 4.0025906735751295,
"config_test": false,
"h... |
__author__ = 'agopalak'
import forecastio
import datetime
import pytz
import json
import os
from geopy import geocoders
import logging
# Setting up logging
logger = logging.getLogger(__name__)
logging.basicConfig(format='%(levelname)s: %(name)s: %(message)s', level=logging.INFO)
# Forecast.io API key
forecastIO_api_... | {
"repo_name": "agopalak/football_pred",
"path": "pre_proc/get_weather.py",
"copies": "1",
"size": "1742",
"license": "mit",
"hash": 642985710849296900,
"line_mean": 27.0967741935,
"line_max": 86,
"alpha_frac": 0.6549942595,
"autogenerated": false,
"ratio": 3.1730418943533696,
"config_test": fal... |
__author__ = 'agopalak'
import nflgame
import csv
import get_weather
import stadium_info
import os.path
import json
import logging
# Setting up logging
logger = logging.getLogger(__name__)
logging.basicConfig(format='%(levelname)s: %(name)s: %(message)s', level=logging.INFO)
# Get NFL data from NFLgame package
def ... | {
"repo_name": "agopalak/football_pred",
"path": "pre_proc/get_nfldata.py",
"copies": "1",
"size": "5551",
"license": "mit",
"hash": 260221441578181600,
"line_mean": 36.2617449664,
"line_max": 137,
"alpha_frac": 0.5379210953,
"autogenerated": false,
"ratio": 3.865598885793872,
"config_test": fal... |
__author__ = 'agostino'
from pycomm.ab_comm.slc import Driver as SlcDriver
import logging
if __name__ == '__main__':
logging.basicConfig(
filename="SlcDriver.log",
format="%(levelname)-10s %(asctime)s %(message)s",
level=logging.DEBUG
)
c = SlcDriver()
if c.open('192.168.1.15')... | {
"repo_name": "bpaterni/pycomm",
"path": "examples/test_slc_only.py",
"copies": "3",
"size": "2520",
"license": "mit",
"hash": -7070770079832689000,
"line_mean": 32.6,
"line_max": 63,
"alpha_frac": 0.4222222222,
"autogenerated": false,
"ratio": 2.9612220916568743,
"config_test": false,
"has_n... |
__author__ = 'agross'
import pandas as pd
import scipy as sp
import numpy as np
import matplotlib.pyplot as plt
from matplotlib import cm
from Figures.Pandas import series_scatter
from Figures.FigureHelpers import init_ax, prettify_ax
from Helpers.Pandas import match_series
def linear_regression(a, b):
a, b = m... | {
"repo_name": "theandygross/Figures",
"path": "src/Figures/Regression.py",
"copies": "1",
"size": "4411",
"license": "mit",
"hash": 8430698963059050000,
"line_mean": 29.0136054422,
"line_max": 78,
"alpha_frac": 0.5477216051,
"autogenerated": false,
"ratio": 2.888670595939751,
"config_test": fal... |
__author__ = 'agross'
import re
import itertools as itertools
import urllib
import pandas as pd
from matplotlib.colors import rgb2hex
from matplotlib.cm import RdBu
KEGG_PATH = 'http://www.kegg.jp/kegg-bin/'
from Figures.KEGG import *
def pull_pathway_info_from_kegg(kegg_id):
o = urllib.urlopen('http://rest.ke... | {
"repo_name": "theandygross/Figures",
"path": "src/Figures/KEGG.py",
"copies": "1",
"size": "2832",
"license": "mit",
"hash": -7338090559315704000,
"line_mean": 32.3176470588,
"line_max": 76,
"alpha_frac": 0.5434322034,
"autogenerated": false,
"ratio": 3.2108843537414966,
"config_test": false,
... |
__author__ = 'agross'
"""
Code taken from MinRK's Gist.
http://nbviewer.ipython.org/gist/minrk/6011986
"""
import io, os, sys, types
#from IPython import nbformat
import nbformat as nbformat
from IPython.core.interactiveshell import InteractiveShell
from IPython.display import display_html
def find_notebook(fullna... | {
"repo_name": "theandygross/NotebookImport",
"path": "NotebookImport.py",
"copies": "1",
"size": "2806",
"license": "apache-2.0",
"hash": 6419217960560200000,
"line_mean": 29.5,
"line_max": 90,
"alpha_frac": 0.5894511761,
"autogenerated": false,
"ratio": 3.886426592797784,
"config_test": false,... |
__author__ = 'agross'
"""
Code taken from MinRK's Gist.
http://nbviewer.ipython.org/gist/minrk/6011986
"""
import io, os, sys, types
from IPython.nbformat import current
from IPython.core.interactiveshell import InteractiveShell
def find_notebook(fullname, path=None):
"""find a notebook, given its fully qualif... | {
"repo_name": "PeterUlz/TCGA_analysis",
"path": "NotebookImport.py",
"copies": "1",
"size": "2547",
"license": "mit",
"hash": -2941774650132067000,
"line_mean": 28.275862069,
"line_max": 86,
"alpha_frac": 0.5928543384,
"autogenerated": false,
"ratio": 3.8826219512195124,
"config_test": false,
... |
__author__ = "aguha@colgate.edu"
from numpy import *
from scipy.integrate import odeint
import matplotlib.pyplot as plt
from matplotlib.backends.backend_pdf import PdfPages
def deriv(vector, t, beta_I, beta_H, beta_F, alpha, gamma_H, gamma_I, gamma_D, gamma_DH, gamma_F, gamma_IH, delta1, delta2, delta3, iota):
... | {
"repo_name": "anindyabd/ebola_eradication",
"path": "diff_eq.py",
"copies": "1",
"size": "3447",
"license": "mit",
"hash": -8056049991920374000,
"line_mean": 45.5810810811,
"line_max": 232,
"alpha_frac": 0.5955903684,
"autogenerated": false,
"ratio": 2.4104895104895103,
"config_test": false,
... |
__author__ = 'aguzun'
from flask import json
import requests
from uwsgi_tasks import task, TaskExecutor
from core import app
SLACK_NOTIFY_HOOK_CONFIG = "SLACK_NOTIFY_HOOK_CONFIG"
@task(executor=TaskExecutor.AUTO)
def notify_camera_state_changed(camera):
# some long running task here
if SLACK_NOTIFY_HOOK_C... | {
"repo_name": "SillentTroll/rascam_server",
"path": "wsgi/notifier.py",
"copies": "1",
"size": "1493",
"license": "apache-2.0",
"hash": -8414024506077243000,
"line_mean": 32.1777777778,
"line_max": 93,
"alpha_frac": 0.6128600134,
"autogenerated": false,
"ratio": 3.779746835443038,
"config_test"... |
__author__ = 'aguzun'
from urlparse import urljoin
import requests
class ControlOption(object):
def __init__(self, option_name):
self.option_name = option_name
self.control_url = "http://localhost:8080" # change the port in motion.config
self.thread_nr = "0" # multiple cameras can be c... | {
"repo_name": "SillentTroll/rascam_client",
"path": "motion/motion_control.py",
"copies": "1",
"size": "1503",
"license": "apache-2.0",
"hash": -3247739610879324000,
"line_mean": 26.8333333333,
"line_max": 97,
"alpha_frac": 0.6141051231,
"autogenerated": false,
"ratio": 3.9448818897637796,
"con... |
__author__ = 'aharvey'
import serial
import string
import ystockquote
import time
INIT = chr(170) + chr(170)+ chr(170)+chr(170)+chr(170)+chr(187)+chr(146)
CLEAR = chr(140) + chr(140)
def cvtStr(msg):
msg = string.replace(msg," ", "%20")
msg = string.replace(msg, ":", " ")
msg = string.replace(... | {
"repo_name": "infamy/ledsignstockticker",
"path": "ticker.py",
"copies": "1",
"size": "1471",
"license": "mit",
"hash": 1075729989474509400,
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"line_max": 72,
"alpha_frac": 0.578518015,
"autogenerated": false,
"ratio": 2.9186507936507935,
"config_test": false,
"has_... |
__author__ = "Ahmad Al-Sajid"
__email__ = "ahmadalsajid@gmail.com"
distance = {
'a': 366,
'b': 0,
'c': 160,
'd': 242,
'e': 161,
'f': 176,
'g': 77,
'h': 151,
'i': 226,
'l': 244,
'm': 241,
'n': 234,
'o': 380,
'p': 10,
'r': 193,
's': 253,
't': 329,
'... | {
"repo_name": "ahmadalsajid/PythonNotes",
"path": "GreedyBestFastSearch.py",
"copies": "1",
"size": "2090",
"license": "mit",
"hash": -8747873405934222000,
"line_mean": 21.2340425532,
"line_max": 95,
"alpha_frac": 0.4933014354,
"autogenerated": false,
"ratio": 2.7718832891246685,
"config_test":... |
__author__ = 'Ahmad Syarif'
import pika
import json
class CommandHandler(object):
avatarKey = 'avatar.NAO.command'
def __init__(self):
credential = pika.PlainCredentials('lumen', 'lumen')
connection = pika.BlockingConnection(pika.ConnectionParameters('localhost', 5672, '/', credential))
... | {
"repo_name": "ahmadsyarif/Python-Agent",
"path": "Command.py",
"copies": "2",
"size": "1230",
"license": "apache-2.0",
"hash": 1528711197354987000,
"line_mean": 38.6774193548,
"line_max": 117,
"alpha_frac": 0.6317073171,
"autogenerated": false,
"ratio": 3.649851632047478,
"config_test": false,... |
__author__ = 'Ahmad Syarif'
import pika
import json
from pydispatch import dispatcher
VISUAL_FACE_DETECTION = 'VISUAL_FACE_DETECTION'
VISUAL_FACE_DETECTION = 'VISUAL_FACE_DETECTION'
VISUAL_FACE_RECOGNITION ='VISUAL_FACE_RECOGNITION'
VISUAL_FACE_TRACKING = 'VISUAL_FACE_TRACKING'
VISUAL_HUMAN_TRACKING = 'VISUAL_HUMAN_TR... | {
"repo_name": "ahmadsyarif/Python-AgentIntelligent",
"path": "Data.py",
"copies": "2",
"size": "8882",
"license": "apache-2.0",
"hash": -1902323057293045200,
"line_mean": 57.4342105263,
"line_max": 172,
"alpha_frac": 0.713578023,
"autogenerated": false,
"ratio": 3.5886868686868687,
"config_test... |
__author__ = 'Ahmed G. Ali'
import dbms
def retrieve_connection(db):
"""
Retrieves Database connection object for a given connection parameters.
:param db: Json object containing connection parameters
:type db: dict
:return: oracle.dbms.Connection object
"""
con = dbm... | {
"repo_name": "arrayexpress/ae_auto",
"path": "dal/oracle/common.py",
"copies": "1",
"size": "2045",
"license": "apache-2.0",
"hash": -5602979348798659000,
"line_mean": 27.8028169014,
"line_max": 123,
"alpha_frac": 0.5828850856,
"autogenerated": false,
"ratio": 4.09,
"config_test": false,
"ha... |
__author__ = 'Ahmed G. Ali'
import MySQLdb as mdb
def retrieve_connection(db):
"""
Retrieves Database connection object for a given connection parameters.
:param db: Json object containing connection parameters
:type db: dict
:return: MySQLDB.Connection object
"""
con = mdb.connect(host=... | {
"repo_name": "arrayexpress/ae_auto",
"path": "dal/mysql/common.py",
"copies": "1",
"size": "1612",
"license": "apache-2.0",
"hash": -9083614565205179000,
"line_mean": 24.1875,
"line_max": 114,
"alpha_frac": 0.6073200993,
"autogenerated": false,
"ratio": 3.688787185354691,
"config_test": false,... |
__author__ = 'Ahmed G. Ali'
f = open('/home/gemmy/E-GEOD-16256_NIH_epigenome_cells_RNA-seq.sdrf.txt', 'r')
lines = f.readlines()
f.close()
extra_header = ['sample_term_id', 'assay_term_id', 'nucleic_acid_term_id', 'Design_description', 'Library_name',
'EDACC_Genboree_Experiment_Page', 'EDACC_Genbor... | {
"repo_name": "arrayexpress/ae_auto",
"path": "misc/extract_combined_columns.py",
"copies": "1",
"size": "1572",
"license": "apache-2.0",
"hash": 945249577302342500,
"line_mean": 43.9142857143,
"line_max": 114,
"alpha_frac": 0.5655216285,
"autogenerated": false,
"ratio": 3.2081632653061223,
"co... |
__author__ = 'Ahmed G. Ali'
def geo_email_parse(email_body):
ids = {}
for word in email_body.split(" "):
word = word.replace(',', '')
if word.startswith('GSE'):
geo_id = word
ae_id = 'E-GEOD-%s' % word.replace('GSE', '')
ids[geo_id] = ae_id
elif word... | {
"repo_name": "arrayexpress/ae_auto",
"path": "utils/email/parser.py",
"copies": "1",
"size": "62039",
"license": "apache-2.0",
"hash": 4712202046893401000,
"line_mean": 25.2210481826,
"line_max": 191,
"alpha_frac": 0.7554763939,
"autogenerated": false,
"ratio": 3.084369096151934,
"config_test"... |
__author__ = 'Ahmed G. Ali'
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='submits and loads sequencing experiment to ENA and ArrayExpress')
parser.add_argument('dir_name', metavar='MAGE-TAB_xxxx', type=str,
help='''The directory name wher... | {
"repo_name": "arrayexpress/ae_auto",
"path": "automation/ena/replace_runs.py",
"copies": "1",
"size": "1426",
"license": "apache-2.0",
"hash": 6264442848415832000,
"line_mean": 66.9523809524,
"line_max": 119,
"alpha_frac": 0.6051893408,
"autogenerated": false,
"ratio": 4.194117647058824,
"conf... |
__author__ = 'Ahmed Hani Ibrahim'
from LearningAlgorithm import *
class Backpropagation(LearningAlgorithm):
def learn(self, learningRate, input, output, network):
"""
:param learningRate: double
:param input: list
:param output: list
:param network: [[Neuron]]
:retu... | {
"repo_name": "AhmedHani/Python-Neural-Networks-API",
"path": "OptimizationAlgorithms/Backpropagation.py",
"copies": "1",
"size": "2236",
"license": "mit",
"hash": -8875118512977930000,
"line_mean": 39.6727272727,
"line_max": 112,
"alpha_frac": 0.5348837209,
"autogenerated": false,
"ratio": 4.454... |
__author__ = 'Ahmed Hani Ibrahim'
from NeuralNetwork.Neuron import Neuron
from ActivationFunctions.Sigmoid import *
import numpy as np
class FeedforwardNeuralNetwork(object):
__numberOfLayers = 0
__numberOfInput = 0
__network = [[Neuron]]
__numberOfNeuronsPerLayer = 0
def __init__(self, numberOfLa... | {
"repo_name": "AhmedHani/Python-Neural-Networks-API",
"path": "NeuralNetwork/FeedforwardNeuralNetwork.py",
"copies": "1",
"size": "3553",
"license": "mit",
"hash": -7092574169344296000,
"line_mean": 31.8981481481,
"line_max": 121,
"alpha_frac": 0.6296087813,
"autogenerated": false,
"ratio": 4.543... |
__author__ = 'Ahmed Hani Ibrahim'
from State import State
from Transition import Transition
class QLearning(object):
def train(self, initState, actions):
currentState = initState
foundState = False
#iterator = iter(actions)
for action in actions:
for transition in cu... | {
"repo_name": "AhmedHani/Deep-Q-Learning",
"path": "DeepQLearning/QLearning.py",
"copies": "1",
"size": "2097",
"license": "mit",
"hash": -5623856706218656000,
"line_mean": 28.5352112676,
"line_max": 108,
"alpha_frac": 0.582260372,
"autogenerated": false,
"ratio": 4.733634311512415,
"config_tes... |
__author__ = 'Ahmed Hani Ibrahim'
from Structures.Cell import Cell
from Structures.Point import Point
from Utilities.Utilities import *
class Astar(object):
__directions = []
__path = [[]]
__source = Cell
__destination = Cell
__map = [[]]
def __init__(self, map):
self.__map = map
... | {
"repo_name": "AhmedHani/Frontier-based-Multi-Agent-Map-Exploration",
"path": "Frontier-based Map Exploration/PathFinder/Astar.py",
"copies": "1",
"size": "4872",
"license": "apache-2.0",
"hash": 8406026599532931000,
"line_mean": 43.6972477064,
"line_max": 109,
"alpha_frac": 0.5632183908,
"autogene... |
__author__ = 'Ahmed Hani Ibrahim'
from Structures.MultipleArmedBandit import MultipleArmedBandit
import numpy as np
class Player(object):
__Q = dict()
__game = 0
__epsilon = 0.0
__numberOfBandits = 0
__numberOfGames = dict()
__rewardValue = 0.0
__saveAction = []
__saveActionValue = []
... | {
"repo_name": "AhmedHani/Banditology",
"path": "Banditology/Player.py",
"copies": "1",
"size": "1956",
"license": "mit",
"hash": -6590180677935649000,
"line_mean": 30.5483870968,
"line_max": 93,
"alpha_frac": 0.5715746421,
"autogenerated": false,
"ratio": 3.873267326732673,
"config_test": false... |
__author__ = 'Ahmed Hani Ibrahim'
import pandas as pnd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sb
def get_train_data():
training_data = pnd.read_csv("./train.csv", header=0, parse_dates=['Dates'])
#training_data = pnd.read_csv("./train.csv", header=0)
return training_data
de... | {
"repo_name": "AhmedHani/Kaggle-Machine-Learning-Competitions",
"path": "Easy/SanFranciscoCrimeClassification/get_data.py",
"copies": "1",
"size": "3368",
"license": "mit",
"hash": -6884163420428945000,
"line_mean": 49.2835820896,
"line_max": 111,
"alpha_frac": 0.6802256532,
"autogenerated": false,... |
__author__ = 'Ahmed Hani Ibrahim'
from read_data import *
import numpy as np
import pickle
from draw_data import *
from get_image import *
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn import svm
labels, train_features = read_train_data(
"G:\\Github Repositories\\KaggleMachin... | {
"repo_name": "AhmedHani/Kaggle-Machine-Learning-Competitions",
"path": "Easy/DigitRecognizer/main.py",
"copies": "1",
"size": "2414",
"license": "mit",
"hash": -5345464781353137000,
"line_mean": 31.6216216216,
"line_max": 148,
"alpha_frac": 0.6694283347,
"autogenerated": false,
"ratio": 2.951100... |
__author__ = 'Ahmed Hani Ibrahim'
from sklearn.cross_validation import cross_val_score
from sklearn.ensemble import RandomForestClassifier
from sklearn.linear_model import LogisticRegression, LinearRegression
from sklearn.naive_bayes import BernoulliNB
from sklearn import svm
from get_data import *
from get_data_2 imp... | {
"repo_name": "AhmedHani/Kaggle-Machine-Learning-Competitions",
"path": "Easy/What's Cooking/linear_svc.py",
"copies": "1",
"size": "1063",
"license": "mit",
"hash": -3008878360568274000,
"line_mean": 26.9736842105,
"line_max": 93,
"alpha_frac": 0.7591721543,
"autogenerated": false,
"ratio": 3.23... |
__author__ = 'Ahmed Hani Ibrahim'
import random
class GeneralizedHebbian(object):
__input = []
__numberOfFeatures = 0
__output = []
__weights = [[]]
__learningRate = 0.0
@property
def Weights(self):
pass
@Weights.getter
def Weights(self):
return self.__weights
... | {
"repo_name": "AhmedHani/Python-Neural-Networks-API",
"path": "DimensionalityReduction/GeneralizedHebbian.py",
"copies": "1",
"size": "2167",
"license": "mit",
"hash": -7934239786521102000,
"line_mean": 30.4057971014,
"line_max": 119,
"alpha_frac": 0.5823719428,
"autogenerated": false,
"ratio": 4... |
__author__ = 'ahmedlawi92@gmail.com'
import json
import requests
import url_constants
class NBAStatsScraper:
player_ids = {}
def __init__(self):
self.populate_players_dict()
def get_player_tracking_stats(self, **kwargs):
base, args = self.build_url(url_constants.player_tracking_url)
... | {
"repo_name": "ahmedlawi92/basketball-stats",
"path": "bballstats/statsnba/stats_nba_scraper.py",
"copies": "1",
"size": "2460",
"license": "apache-2.0",
"hash": 777384893442659700,
"line_mean": 33.6478873239,
"line_max": 93,
"alpha_frac": 0.6235772358,
"autogenerated": false,
"ratio": 3.59124087... |
__author__ = 'ahmedlawi92@gmail.com'
import json
import string
from enum import Enum
from bs4 import BeautifulSoup
import requests
class BBRefScraper:
__base_url = 'http://www.basketball-reference.com{s}'
__url_key = 'info_page'
def __init__(self, json_file):
self.players = json.load(file(json_... | {
"repo_name": "ahmedlawi92/basketball-stats",
"path": "bballstats/bballreference/bbref_scraper.py",
"copies": "1",
"size": "2169",
"license": "apache-2.0",
"hash": -3160957800398579700,
"line_mean": 31.3731343284,
"line_max": 139,
"alpha_frac": 0.6090364223,
"autogenerated": false,
"ratio": 3.362... |
__author__ = 'Ahmed'
from pymongo import MongoClient
import json
import re
from os import listdir
from os.path import isfile, join
client = MongoClient()
db = client.hotelinfo
j = 0
for i in [ f for f in listdir('json') if isfile(join('json',f)) ]:
if i.find(".json") == -1:
continue
print i
... | {
"repo_name": "ahmedshabib/evergreen-gainsight-hack",
"path": "mongodumper.py",
"copies": "1",
"size": "1275",
"license": "mit",
"hash": 8569189593942164000,
"line_mean": 26.7173913043,
"line_max": 71,
"alpha_frac": 0.5262745098,
"autogenerated": false,
"ratio": 3.581460674157303,
"config_test"... |
__author__ = 'Ahmed'
import time
import calendar
from flask import Flask, request, session, g, redirect, url_for, render_template, flash
from pymongo import MongoClient
import json
import uuid
from werkzeug.utils import secure_filename
from werkzeug.security import check_password_hash, generate_password_hash
import ran... | {
"repo_name": "ahmedshabib/evergreen-gainsight-hack",
"path": "webapi.py",
"copies": "1",
"size": "2483",
"license": "mit",
"hash": -5947337194787184000,
"line_mean": 30.4303797468,
"line_max": 87,
"alpha_frac": 0.6653242046,
"autogenerated": false,
"ratio": 3.069221260815822,
"config_test": fa... |
__author__ = 'ahmed'
import boto3, argparse, yaml
from time import sleep
import os.path
def tag_instances(awsTags):
reservations = ec2Client.describe_instances()
instances = [ i['Instances'] for i in reservations['Reservations']]
# Iterate EC2 instances ...
# if instance is part of Clou... | {
"repo_name": "borkit/scriptdump",
"path": "AWS/tag_aw_resources.py",
"copies": "1",
"size": "11248",
"license": "mit",
"hash": 2657007533907976700,
"line_mean": 42.2834645669,
"line_max": 138,
"alpha_frac": 0.5090682788,
"autogenerated": false,
"ratio": 4.366459627329193,
"config_test": false,... |
import json, time, logging
from os import path, getcwd, system, chdir
from sys import stdout
from shutil import copyfile
from subprocess import check_call, STDOUT, DEVNULL
from update_values_helpers import *
logging.basicConfig(stream=stdout, level=logging.INFO)
logger = logging.getLogger("build_resume")
# set abso... | {
"repo_name": "atla5/resume",
"path": "src/build_resume.py",
"copies": "1",
"size": "6189",
"license": "mit",
"hash": -1120989275830357100,
"line_mean": 32.6141304348,
"line_max": 114,
"alpha_frac": 0.6740501213,
"autogenerated": false,
"ratio": 3.5586881472957423,
"config_test": false,
"has_... |
import logging
logger = logging.getLogger(__name__)
months = ["Jan", "Feb", "March", "April", "May", "June", "July", "Aug", "Sept", "Oct", "Nov", "Dec"]
months_full = ["January", "February", "March", "April", "May", "June", "July", "August", "September", "October", "November", "December"]
def humanize_date(yyyy_mm,... | {
"repo_name": "atla5/resume",
"path": "src/update_values_helpers.py",
"copies": "1",
"size": "5687",
"license": "mit",
"hash": -5475702164675141000,
"line_mean": 36.9133333333,
"line_max": 136,
"alpha_frac": 0.6135044839,
"autogenerated": false,
"ratio": 3.6199872692552515,
"config_test": false... |
__author__ = "Aishwarya Sharma"
# This class represents the "posts" table in the blog database.
class Post:
def __init__(self, post_id=None, title=None, content=None, create_date=None, edit_date=None, summary=None):
self.post_id = post_id
self.title = title
self.summary = summary
... | {
"repo_name": "aishsharma/Weirdo_Blog",
"path": "src/database/tables.py",
"copies": "1",
"size": "1518",
"license": "mit",
"hash": 4089009249247639000,
"line_mean": 34.1428571429,
"line_max": 111,
"alpha_frac": 0.5177865613,
"autogenerated": false,
"ratio": 3.9224806201550386,
"config_test": fa... |
__author__ = 'Ajay'
from django.conf.urls import url, patterns, include
from . import views
from blog.views import Index, PeopleList
from django.contrib import admin
admin.autodiscover()
urlpatterns = [
#url(r'^$', views.post_list, name='post_list'),
url(r'^hello$', views.hello, name='post_list'),
url (r'^... | {
"repo_name": "ajaycode/django1",
"path": "blog/urls.py",
"copies": "1",
"size": "1338",
"license": "apache-2.0",
"hash": 53830016138077830,
"line_mean": 45.1724137931,
"line_max": 108,
"alpha_frac": 0.6434977578,
"autogenerated": false,
"ratio": 2.966740576496674,
"config_test": false,
"has_... |
__author__ = 'Ajay'
import re, collections
def words(text): return re.findall('[a-z]+', text.lower())
def train(features):
model = collections.defaultdict(lambda: 1)
for f in features:
model[f] += 1
return model
NWORDS = train(words(open('big.txt').read()))
alphabet = 'abcdefghijklmnopqrstuvwxy... | {
"repo_name": "ajaycode/django1",
"path": "blog/spell_check.py",
"copies": "1",
"size": "1108",
"license": "apache-2.0",
"hash": -9200422109785398000,
"line_mean": 29.8055555556,
"line_max": 85,
"alpha_frac": 0.6263537906,
"autogenerated": false,
"ratio": 2.9546666666666668,
"config_test": fals... |
#********************************************List of Dependencies*******************************************************
#The following code has been tested with the indicated versions on 64bit Linux and PYTHON 2.7.3
#os: Use standard library with comes with python.
#pint: 0.5.1
#************************************... | {
"repo_name": "DaisukeMiyamoto/python-Lmeasure",
"path": "LMIO/util/morphometricMeasurements.py",
"copies": "1",
"size": "2053",
"license": "apache-2.0",
"hash": 8812055204942862000,
"line_mean": 37.037037037,
"line_max": 152,
"alpha_frac": 0.5353141744,
"autogenerated": false,
"ratio": 3.7531992... |
#********************************************List of Dependencies*******************************************************
#The following code has been tested with the indicated versions on 64bit Linux and PYTHON 2.7.3
#blender: 2.6.9
#***********************************************************************************... | {
"repo_name": "dEvasEnApati/BlenderSWCVizualizer",
"path": "blenderHelper.py",
"copies": "2",
"size": "24404",
"license": "apache-2.0",
"hash": 7811701832075533000,
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"line_max": 475,
"alpha_frac": 0.5535977709,
"autogenerated": false,
"ratio": 4.022416350749959,
"conf... |
__author__ = 'ajdanelz'
import subprocess
from datetime import *
from dateutil.relativedelta import relativedelta
pipeline = []
pipeline.append("git tag")
pipeline.append("xargs -I@ git log --format=format:'%ai @%n' -1 @")
pipeline.append("sort")
pipeline.append("awk '{print $1,$4}'")
command = "|".join(pipeline)
out... | {
"repo_name": "scoobah36/GitVersionParsing",
"path": "VersionsByDate.py",
"copies": "1",
"size": "1790",
"license": "mit",
"hash": -2286020013892744200,
"line_mean": 26.5384615385,
"line_max": 90,
"alpha_frac": 0.5530726257,
"autogenerated": false,
"ratio": 3.4225621414913956,
"config_test": fa... |
__author__ = 'aje'
#
# Copyright (c) 2008 - 2013 10gen, Inc. <http://10gen.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless re... | {
"repo_name": "jac2130/BettingIsBelieving",
"path": "Betting/putsDAO.py",
"copies": "1",
"size": "3977",
"license": "mit",
"hash": -542300794793250700,
"line_mean": 33.2844827586,
"line_max": 125,
"alpha_frac": 0.561981393,
"autogenerated": false,
"ratio": 3.702979515828678,
"config_test": fals... |
__author__ = 'aje'
#
# Copyright (c) 2008 - 2013 10gen, Inc. <http://10gen.com>
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless r... | {
"repo_name": "KartikKannapur/MongoDB_M101P",
"path": "Week_2/homework/homework_2_3/login_logout_signup/sessionDAO.py",
"copies": "1",
"size": "2481",
"license": "mit",
"hash": 7494334837058727000,
"line_mean": 26.8764044944,
"line_max": 85,
"alpha_frac": 0.6565900846,
"autogenerated": false,
"ra... |
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