text stringlengths 0 1.05M | meta dict |
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from copy import deepcopy
import numpy as np
from scipy import linalg
from ..io.constants import FIFF
from ..io.pick import pick_channels
from ..utils import logger, verbose, _check_option
from ..forward import convert_forward_solution
from ..evoked import EvokedArray
from ..source_estimate import SourceEstimate
fro... | {
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from copy import deepcopy
import numpy as np
from scipy import linalg
from ..io.constants import FIFF
from ..io.pick import pick_channels
from ..utils import logger, verbose
from ..forward import convert_forward_solution
from ..evoked import EvokedArray
from ..source_estimate import SourceEstimate
from .inverse impo... | {
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from copy import deepcopy
import numpy as np
from scipy import linalg
from ..io.pick import pick_channels
from ..utils import logger, verbose
from ..forward import convert_forward_solution
from ..evoked import EvokedArray
from ..source_estimate import SourceEstimate
from .inverse import _subject_from_inverse
from . ... | {
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from copy import deepcopy
import numpy as np
from scipy import linalg
from ..utils import logger, verbose
from ..io.constants import FIFF
from ..evoked import EvokedArray
from ..source_estimate import SourceEstimate
from .inverse import _subject_from_inverse
from . import apply_inverse
@verbose
def point_spread_fu... | {
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__author__ = 'SolarLune'
from bge import logic
import mathutils
from . import window
ML_LOCK_NONE = 0 # Constants for mouse-look axis locking
ML_LOCK_X = 1
ML_LOCK_Y = 2
# TODO: Add axis setting to allow for joystick analog controlled mouse-look
def mouse_look(obj=None, accel_speed=1.0, max_speed=45.0, frictio... | {
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__author__ = 'SolarLune'
from bge import logic
LT_POINT = 0
LT_SPOT = 1
LT_SUN = 2
LT_HEMI = 3
class LightManager():
"""
A class for managing lights.
The Poll() function looks through the scene to find objects that have a property named "DynLightNode", which indicates
spots where lights should be ... | {
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__author__ = 'SolarLune'
from bge import logic, render, events
def get_aspect_ratio(width_first=True):
"""
Returns the aspect ratio of the game window, or the window's width / the window's height.
If width_first is True (default), then it will return the window's height / the window's width.
:param w... | {
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__author__ = 'SolarLune'
from bge import logic, render
import math,time
# Nodemaps for game space navigation.
class Node():
def __init__(self, obj):
self.obj = obj
self.obj['nm_node'] = self # Place yourself in the "owner" game object
self.position = self.obj.worldPosition
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__author__ = 'SolarLune'
from bge import logic, types
class Trail():
def __init__(self, stretch=True, spacing=3, reverse=False, vertex_axis="y",
update_on_moving_only=False, trail_target_obj=None, trail_obj=None):
"""
Creates a Trail object. Trail objects influence the vertices ... | {
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__author__ = 'SolarLune'
import collections
import bge
class CViewManager():
"""
A static class that you don't have to instantiate to use.
"""
AH_NONE = 0
AH_VERTICAL = 1 # Increases from first camera added at top to last camera added at bottom
AH_VERTICAL_REVERSED = 2 # Goes the other w... | {
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__author__ = 'SolarLune'
import math
import time
import collections
import operator
from bge import logic, constraints, types, render
import mathutils
from .math import sign
lib_new_counter = 0
# Helper classes
class Polygon():
def __init__(self, polygon):
self.poly_data = polygon
self.vert... | {
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__author__ = 'SolarLune'
import math
from bge import logic
# input constants
KEY = 0 # Different input types; Keyboard key
JOYBUTTON = 1 # Joystick button
JOYHAT = 2 # Joystick hat (D-Pad on a lot of controllers)
JOYAXIS = 3 # Joystick axes (sticks and triggers on a 360 controller)
MOUSEAXIS = 4 # Mouse moveme... | {
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__author__ = 'SolarLune'
import math
from bge import logic
class _spritemap_base():
def __init__(self, obj):
"""
Creates a SpriteMap to animate an object by.
:param obj: KX_GameObject - Sprite object to perform operations on
:return: _spritemap_base
"""
self.o... | {
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__author__ = 'SolarLune'
import mathutils
from bge import logic, render
def lerp(a, b, scalar):
"""Lerp - Linearly interpolates between 'a'
when 'scalar' is 0 and 'b' when 'scalar' is 1.
a = number or Vector
b = number or Vector
scaler = number between 0 and 1
"""
return (a + scalar * (b... | {
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__author__ = 'SolarLune'
import os
import random
import aud
from bge import logic
class AudioDevice():
BGM_STATUS_STOPPED = 0
BGM_STATUS_FADING_IN = 1
BGM_STATUS_PLAYING = 2
BGM_STATUS_FADING_OUT = 3
def __init__(self, sound_folder='//assets/snd/'):
self.device = aud.device()
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__author__ = 'SolarLune'
import sys
from bge import logic
class Scenes:
def __str__(self):
return str(logic.getSceneList())
def __len__(self):
return len(logic.getSceneList())
def __iter__(self):
return iter(logic.getSceneList())
def __contains__(self, key):
if isi... | {
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__author__ = 'SolarLune'
# Random level generation module.
# TODO: Add rectangular rooms to the GenNodes room styles.
import random
import math
import copy
from bge import logic
import mathutils
from .mesh import get_dimensions
from .math import clamp
# CONSTANTS
# Connection styles for the GenNodes function;
GN... | {
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__author__ = 'SolarLune'
# This is a library of 2D filters that either
# 1) I made, or
# 2) I Adapted from other scripts (I don't think any were direct copies, just re-adaptation. If I am wrong, though,
# please point it out to me).
#
# Hopefully someone can use them.
# As a note to myself, the 2D filter system in th... | {
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from __future__ import print_function
import argparse
try:
from collections import OrderedDict
except:
from ordereddict import OrderedDict
import json
import os
import traceback
from time import time
import logging
from jinja2 import Template
import numpy as np
import matplotlib.pyplot as plt
# imports for m... | {
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from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
... | {
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from sys import version_info
import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
fro... | {
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import numpy as np
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing imp... | {
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import numpy as np
import pytest
from scipy import interpolate, sparse
from copy import deepcopy
from sklearn.datasets import load_boston
from sklearn.exceptions import ConvergenceWarning
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.uti... | {
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import warnings
from sys import version_info
import numpy as np
from scipy import interpolate
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import SkipTest
from sklearn.utils.... | {
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import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn... | {
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import numpy as np
from scipy import sparse
from scipy import linalg
from scipy import stats
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklear... | {
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__author__ = 'Somebody'
# -*- coding: utf-8 -*-
__date__ = "05/ 04 / 2015"
import csv
class LectureCSV:
def __init__(self, fichier):
"""
:param fichier: le nom du fichier csv à importer
"""
self.fichier = fichier
def importation(self):
chars = []
"""
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__author__ = 'Somebody'
import numpy as np
def myfun(x, binary=True):
"""
fonction de seuillage
si > theta : 1
si < theta : min
sinon theta
"""
if binary:
_min, _theta = 0, .5
else:
_min, _theta = -1, 0
if x > _theta: return 1
if x == _theta: return _theta
r... | {
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"has_no_keywo... |
__author__ = 'sondredyvik'
from common import astar
from collections import deque
from heapq import heappush, heappop
class Astarmod1(astar.Astar):
def __init__(self, type, boardobject):
self.type = type
self.board = boardobject
super(Astarmod1, self).__init__()
def arc_cost(self, c... | {
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__author__ = 'sondredyvik'
from common import state
import copy
class CspState(state.State):
def __init__(self, domains):
self.domains = domains
super(CspState, self).__init__()
#If the domain of any variable is zero, it is a contradictory state
def check_if_contradictory(self):
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__author__ = 'sondredyvik'
from common import state
class StateMod1(state.State):
##The methods in this document have names that explain them
def __init__(self, xpos, ypos, board, parent):
self.board = board
self.dimensions = board.dimensions
self.xpos = xpos
self.ypos = ypos
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__author__ = 'sondredyvik'
from heapq import heappop, heappush
class State(object):
def __init__(self):
# Initialises the searchstate. It has to know about board to calculate neighbours
self.h = float('inf')
self.g = float('inf')
self.nodes_created = 0
self.children = []
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__author__ = 'sondredyvik'
from Tkinter import *
from tkFileDialog import askopenfilename
from nonogram import NonoAstarGac
import cProfile
class Gui:
def __init__(self, parent,width=800, height=800 ):
self.width = width
self.height = height
self.parent = Frame(parent, width =self.width, h... | {
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"alpha_frac": 0.5977944236,
"autogenerated": false,
"ratio": 3.445161290322581,
"config_te... |
__author__ = 'sondredyvik'
from Tkinter import *
from tkFileDialog import askopenfilename
from gac import GAC
from common import constraint as cspconstraint
from common import constraintnet
import variable as cspvariable
import astarmod2
import time
import cProfile
class csp_gui:
def __init__(self, parent):
... | {
"repo_name": "pmitche/it3105-aiprogramming",
"path": "project1/module2/csp_gui.py",
"copies": "1",
"size": "7966",
"license": "mit",
"hash": -6789642456005787000,
"line_mean": 41.8279569892,
"line_max": 138,
"alpha_frac": 0.6112226965,
"autogenerated": false,
"ratio": 3.3811544991511036,
"conf... |
__author__ = 'soobash'
import os,sys
from skimage import io
from skimage import measure
import numpy as np
### define functions ######################
# incomplete, have to be written in full
def universal_quality_index(P,Q):
block_size = 8
P = int (P)
Q = int (Q)
N = block_size**2
s= (N,N)
... | {
"repo_name": "JeromeRisselin/PRJ-medtec_sigproc",
"path": "echopen-leaderboard/bootcamp/leaderboard/metrics.py",
"copies": "5",
"size": "3627",
"license": "mit",
"hash": 1464394338207511000,
"line_mean": 24.7234042553,
"line_max": 84,
"alpha_frac": 0.5329473394,
"autogenerated": false,
"ratio": ... |
'''Author: Sourabh Bajaj'''
import ez_setup
ez_setup.use_setuptools()
from setuptools import setup, find_packages
setup(
name='QSTK',
version='0.2.8',
author='Sourabh Bajaj',
packages=find_packages(),
namespace_packages=['QSTK'],
include_package_data=True,
long_description=open('README.md')... | {
"repo_name": "wogsland/QSTK",
"path": "setup.py",
"copies": "3",
"size": "1344",
"license": "bsd-3-clause",
"hash": 4640457079178201000,
"line_mean": 31.7804878049,
"line_max": 64,
"alpha_frac": 0.5944940476,
"autogenerated": false,
"ratio": 3.652173913043478,
"config_test": false,
"has_no_k... |
__author__ = 'sourabhdesai'
import nltk
import json
import re
from nltk.corpus import stopwords
# nltk.download()
def lowercase(tokens):
return [w.lower() for w in tokens] #change to lower case
def removeStopWords(tokens):
return [ word for word in tokens if word not in stopwords.words('english') ]
def remov... | {
"repo_name": "sourabhdesai/CS-398-VL-MP1",
"path": "book_nlp.py",
"copies": "1",
"size": "1293",
"license": "mit",
"hash": 9055920202713293000,
"line_mean": 32.1794871795,
"line_max": 98,
"alpha_frac": 0.6798143852,
"autogenerated": false,
"ratio": 3.0639810426540284,
"config_test": false,
"... |
__author__ = 'Spasley'
from model.group import Group
class GroupHelper:
def __init__(self, app):
self.app = app
def open_groups_page(self):
# open groups page
wd = self.app.wd
if not (wd.current_url.endswith("/group.php") and len(wd.find_elements_by_name("new")) > 0):
... | {
"repo_name": "Spasley/python",
"path": "fixture/group.py",
"copies": "1",
"size": "3809",
"license": "apache-2.0",
"hash": 8787744071381417000,
"line_mean": 31.8448275862,
"line_max": 100,
"alpha_frac": 0.5886059333,
"autogenerated": false,
"ratio": 3.391807658058771,
"config_test": false,
"... |
__author__ = 'Spasley'
from model.recordfields import RecordFields
import random
def test_record_del(app, db, check_ui):
if len(db.get_record_list()) == 0:
app.record.filling_form(RecordFields(firstname='Test'))
old_records = db.get_record_list()
record = random.choice(old_records)
record_inde... | {
"repo_name": "Spasley/python",
"path": "test/test_modify_record.py",
"copies": "1",
"size": "1600",
"license": "apache-2.0",
"hash": -3457357190002987000,
"line_mean": 63.04,
"line_max": 134,
"alpha_frac": 0.556875,
"autogenerated": false,
"ratio": 3.6281179138321997,
"config_test": false,
"... |
__author__ = 'Spasley'
from model.recordfields import RecordFields
import re
from selenium.webdriver.support.select import Select
import random
from random import randrange
class RecordHelper:
def __init__(self, app):
self.app = app
def open_records_page(self):
# open records page
wd... | {
"repo_name": "Spasley/python",
"path": "fixture/record.py",
"copies": "1",
"size": "9260",
"license": "apache-2.0",
"hash": -4021511362470668300,
"line_mean": 42.4741784038,
"line_max": 116,
"alpha_frac": 0.6166306695,
"autogenerated": false,
"ratio": 3.409425625920471,
"config_test": false,
... |
__author__ = 'Spasley'
from sys import maxsize
class RecordFields:
def __init__(self, firstname=None, lastname=None, middlename=None, nickname=None, title=None,
company=None, address=None, address2=None, home=None, mobile=None, work=None, fax=None, homepage=None, phone2=None,
no... | {
"repo_name": "Spasley/python",
"path": "model/recordfields.py",
"copies": "1",
"size": "1865",
"license": "apache-2.0",
"hash": -8435667842889479000,
"line_mean": 35.5882352941,
"line_max": 140,
"alpha_frac": 0.6,
"autogenerated": false,
"ratio": 3.607350096711799,
"config_test": false,
"has... |
__author__ = 'Spasley'
class SessionHelper:
def __init__(self, app):
self.app = app
def login(self, username, password):
# login
wd = self.app.wd
self.app.open_home_page()
wd.find_element_by_name("user").click()
wd.find_element_by_name("user").clear()
w... | {
"repo_name": "Spasley/python",
"path": "fixture/session.py",
"copies": "1",
"size": "1453",
"license": "apache-2.0",
"hash": -4873294602825145000,
"line_mean": 28.6734693878,
"line_max": 75,
"alpha_frac": 0.557467309,
"autogenerated": false,
"ratio": 3.386946386946387,
"config_test": false,
... |
__authors__ = 'Pat and Tony'
import random
import Player
import Message
class PBATPlayer(Player.Player):
# self variables
player_list = []
rock_cut = .3
paper_cut = .6
total = 10
name = None
# finds a player and his information
def find_player(self, person):
found_player = Fa... | {
"repo_name": "geebzter/game-framework",
"path": "PBATPlayer.py",
"copies": "1",
"size": "3439",
"license": "apache-2.0",
"hash": -1772350514542904000,
"line_mean": 28.4017094017,
"line_max": 90,
"alpha_frac": 0.5644082582,
"autogenerated": false,
"ratio": 3.682012847965739,
"config_test": fals... |
import requests, json, math, os, sys
import numpy
import cv2
from scipy.spatial import Delaunay
from PIL import Image
import xml.etree.ElementTree as ET
import shapely.geometry
import shapely.geometry.polygon
from common import getStringRangeToArray, getRange, getBoundingBox, remap, remapPoints, remapIPoints, isInBo... | {
"repo_name": "mapzen/terrarium",
"path": "data/terrarium.py",
"copies": "1",
"size": "14113",
"license": "mit",
"hash": -4161509638660289500,
"line_mean": 37.0404312668,
"line_max": 117,
"alpha_frac": 0.5508396514,
"autogenerated": false,
"ratio": 3.9632125807357483,
"config_test": false,
"h... |
import csv
import sys
def hasBottomLeft(row):
if int(row[0]) == 1:
row.append(1)
elif int(row[0]) == 2:
row.append(-1)
else:
row.append(0)
def middleThreeColumns(row):
p1 = 0
p2 = 0
for piece in range(12,29):
if int(row[piece]) == 1:
p1 = p... | {
"repo_name": "PatandFioneTakeAI/Weka",
"path": "heuristic.py",
"copies": "1",
"size": "3429",
"license": "unlicense",
"hash": -7405204160777774000,
"line_mean": 27.8235294118,
"line_max": 74,
"alpha_frac": 0.4983960338,
"autogenerated": false,
"ratio": 3.6362672322375396,
"config_test": false,... |
import time
from ustruct import unpack, unpack_from
from array import array
# BME280 default address.
BME280_I2CADDR = 0x76
# Operating Modes
BME280_OSAMPLE_1 = 1
BME280_OSAMPLE_2 = 2
BME280_OSAMPLE_4 = 3
BME280_OSAMPLE_8 = 4
BME280_OSAMPLE_16 = 5
BME280_REGISTER_CONTROL_HUM = 0xF2
BME280_REGISTER_CONTROL = 0xF4
... | {
"repo_name": "PinkInk/iocp",
"path": "sensor-bh1750-bme280-hygrometer/lib/bme280.py",
"copies": "2",
"size": "8114",
"license": "mit",
"hash": 1187223847851717400,
"line_mean": 36.5648148148,
"line_max": 80,
"alpha_frac": 0.5650727138,
"autogenerated": false,
"ratio": 3.2288101870274573,
"conf... |
from __future__ import division
import warnings
from warnings import warn
from abc import ABC
from abc import abstractmethod
import numpy as np
from scipy.sparse import issparse
from scipy.sparse import hstack as sparse_hstack
from ..base import ClassifierMixin, RegressorMixin
from .base import BaseEnsemble, _part... | {
"repo_name": "psarka/uplift",
"path": "uplift/ensemble/forest.py",
"copies": "1",
"size": "60915",
"license": "bsd-3-clause",
"hash": 1097201284932917000,
"line_mean": 38.2746615087,
"line_max": 93,
"alpha_frac": 0.6036772552,
"autogenerated": false,
"ratio": 4.310125238802802,
"config_test": ... |
"""
First-order ODE integrators.
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
i = 0, ..., len(y0) - 1
class ode
---------
A generic ... | {
"repo_name": "minhlongdo/scipy",
"path": "scipy/integrate/_ode.py",
"copies": "3",
"size": "42178",
"license": "bsd-3-clause",
"hash": 773482070358490900,
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"line_max": 90,
"alpha_frac": 0.5433164209,
"autogenerated": false,
"ratio": 3.671163721820872,
"config_test": ... |
"""
First-order ODE integrators
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
i = 0, ..., len(y0) - 1
class ode
---------
A generic i... | {
"repo_name": "stefanv/scipy3",
"path": "scipy/integrate/ode.py",
"copies": "1",
"size": "25739",
"license": "bsd-3-clause",
"hash": -5441868968838015000,
"line_mean": 31.0935162095,
"line_max": 93,
"alpha_frac": 0.5461362135,
"autogenerated": false,
"ratio": 3.4820075757575757,
"config_test": ... |
"""
First-order ODE integrators.
User-friendly interface to various numerical integrators for solving a
system of first order ODEs with prescribed initial conditions::
d y(t)[i]
--------- = f(t,y(t))[i],
d t
y(t=0)[i] = y0[i],
where::
i = 0, ..., len(y0) - 1
class ode
---------
A generic ... | {
"repo_name": "josephcslater/scipy",
"path": "scipy/integrate/_ode.py",
"copies": "4",
"size": "44051",
"license": "bsd-3-clause",
"hash": 8441206910124214000,
"line_mean": 33.3611544462,
"line_max": 90,
"alpha_frac": 0.544845747,
"autogenerated": false,
"ratio": 3.7258732978093545,
"config_tes... |
__author__ = 'Spencer Dodd' # I almost don't want to take credit for this pasta
# NOTE: It's spaghetti. God help your soul if you need to debug or maintain this.
# to block SeqIO error about it being an experimental module
# probably should get a more permanent (and safe) fix
import warnings
warnings.filterwarnings("... | {
"repo_name": "SpencerDodd/CrossBLAST",
"path": "blast_accession.py",
"copies": "1",
"size": "32910",
"license": "mit",
"hash": -7326281932975909000,
"line_mean": 26.3566084788,
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"alpha_frac": 0.6631419022,
"autogenerated": false,
"ratio": 3.101206181681116,
"config_test": false... |
__author__ = 'spersinger'
import base64
import time
import struct
import traceback
import pdb
from .bit_packing import BitPacking
from .configuration import Configuration
from .encryption import Encryption
from .secret import Secret
class Token:
class InvalidToken(ValueError):
def __init__(self, message):... | {
"repo_name": "heroku/fernet-py",
"path": "fernet/token.py",
"copies": "1",
"size": "5706",
"license": "mit",
"hash": -8377189836481412000,
"line_mean": 32.1744186047,
"line_max": 97,
"alpha_frac": 0.5937609534,
"autogenerated": false,
"ratio": 4.116883116883117,
"config_test": false,
"has_no... |
__author__ = 'spersinger'
import os
try:
import M2Crypto
from M2Crypto.EVP import Cipher, HMAC
except ImportError:
# Fall back to pycrypto
from Crypto.Cipher import AES
from Crypto.Hash import SHA256
from Crypto.Hash.HMAC import HMAC
class Encryption:
AES_BLOCK_SIZE = 16
DECODE = 0
... | {
"repo_name": "heroku/fernet-py",
"path": "fernet/encryption.py",
"copies": "1",
"size": "2468",
"license": "mit",
"hash": 5633653606298164000,
"line_mean": 28.734939759,
"line_max": 82,
"alpha_frac": 0.6154781199,
"autogenerated": false,
"ratio": 3.58200290275762,
"config_test": false,
"has_... |
__author__ = 'spersinger'
import re
import pdb
import string
import random
import os.path
from enum import Enum
from itertools import imap
from functools import partial
from datetime import datetime, timedelta
# Field types
def load_file(name):
return [line.rstrip() for line in open(os.path.join(os.path.dirname(_... | {
"repo_name": "heroku/salesforce-utils",
"path": "salesforce_utils/data/record_generator.py",
"copies": "2",
"size": "7765",
"license": "mit",
"hash": -6296823234920252000,
"line_mean": 27.6531365314,
"line_max": 107,
"alpha_frac": 0.599613651,
"autogenerated": false,
"ratio": 3.7385652383245067,... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
from abc import ABCMeta, abstractmethod
import numpy as np
from .sgd_fast import EpsilonInsensitive
from .sgd_fast import Hinge
from .sgd_fast import Huber
from .sgd_fast import Log
from .sgd_fast import ModifiedHuber
from .sgd_fast import S... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scikit-learn-master/sklearn/linear_model/stochastic_gradient.py",
"copies": "1",
"size": "50707",
"license": "mit",
"hash": 4993240463504691000,
"line_mean": 40.0582995951,
"line_max": 79,
"alpha_frac": 0.5532569468,
"autogenerated": fal... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
import warnings
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from ..base import BaseEstimator... | {
"repo_name": "ankurankan/scikit-learn",
"path": "sklearn/linear_model/stochastic_gradient.py",
"copies": "1",
"size": "50480",
"license": "bsd-3-clause",
"hash": -7876837582741154000,
"line_mean": 40.1074918567,
"line_max": 85,
"alpha_frac": 0.5549722662,
"autogenerated": false,
"ratio": 4.31932... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
import warnings
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from ..base import BaseEstimator... | {
"repo_name": "Sklearn-HMM/scikit-learn-HMM",
"path": "sklean-hmm/linear_model/stochastic_gradient.py",
"copies": "3",
"size": "42863",
"license": "bsd-3-clause",
"hash": -6474025814889812000,
"line_mean": 39.2469483568,
"line_max": 79,
"alpha_frac": 0.5630030562,
"autogenerated": false,
"ratio":... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from ..base import BaseEstimator, RegressorMixin... | {
"repo_name": "chaluemwut/fbserver",
"path": "venv/lib/python2.7/site-packages/sklearn/linear_model/stochastic_gradient.py",
"copies": "1",
"size": "42867",
"license": "apache-2.0",
"hash": 6050011072785426000,
"line_mean": 38.9878731343,
"line_max": 79,
"alpha_frac": 0.565399958,
"autogenerated": ... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from .base import make_dataset
from ..base import BaseEsti... | {
"repo_name": "raghavrv/scikit-learn",
"path": "sklearn/linear_model/stochastic_gradient.py",
"copies": "3",
"size": "55145",
"license": "bsd-3-clause",
"hash": -6046899954372989000,
"line_mean": 40.462406015,
"line_max": 79,
"alpha_frac": 0.5559162209,
"autogenerated": false,
"ratio": 4.31157154... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel, delayed
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_datas... | {
"repo_name": "bnaul/scikit-learn",
"path": "sklearn/linear_model/_stochastic_gradient.py",
"copies": "2",
"size": "65464",
"license": "bsd-3-clause",
"hash": 2704018222622182400,
"line_mean": 40.0690087829,
"line_max": 79,
"alpha_frac": 0.5907827203,
"autogenerated": false,
"ratio": 4.2256648592... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import make_dataset
from .... | {
"repo_name": "ndingwall/scikit-learn",
"path": "sklearn/linear_model/_stochastic_gradient.py",
"copies": "2",
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"line_mean": 40.068338558,
"line_max": 79,
"alpha_frac": 0.5908188813,
"autogenerated": false,
"ratio": 4.225519... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from ..utils import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from .base import make_dataset
from ..base import BaseEstimator, Regr... | {
"repo_name": "vortex-ape/scikit-learn",
"path": "sklearn/linear_model/stochastic_gradient.py",
"copies": "2",
"size": "64475",
"license": "bsd-3-clause",
"hash": -1979827478802195500,
"line_mean": 40.3566388711,
"line_max": 79,
"alpha_frac": 0.5769367972,
"autogenerated": false,
"ratio": 4.21102... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
from abc import ABCMeta, abstractmethod
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin, SparseCoefMixin
from .base import make_dataset
from ..base import BaseEstimator, Regressor... | {
"repo_name": "giorgiop/scikit-learn",
"path": "sklearn/linear_model/stochastic_gradient.py",
"copies": "20",
"size": "51086",
"license": "bsd-3-clause",
"hash": -5905984327763754000,
"line_mean": 40,
"line_max": 79,
"alpha_frac": 0.5532044004,
"autogenerated": false,
"ratio": 4.321630995685644,
... |
"""Classification, regression and One-Class SVM using Stochastic Gradient
Descent (SGD).
"""
import numpy as np
import warnings
from abc import ABCMeta, abstractmethod
from joblib import Parallel
from ..base import clone, is_classifier
from ._base import LinearClassifierMixin, SparseCoefMixin
from ._base import mak... | {
"repo_name": "kevin-intel/scikit-learn",
"path": "sklearn/linear_model/_stochastic_gradient.py",
"copies": "2",
"size": "82378",
"license": "bsd-3-clause",
"hash": -7982175830328294000,
"line_mean": 39.2629521017,
"line_max": 79,
"alpha_frac": 0.5859574158,
"autogenerated": false,
"ratio": 4.222... |
"""Classification and regression using Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
from abc import ABCMeta, abstractmethod
import warnings
from ..externals.joblib import Parallel, delayed
from .base import LinearClassifierMixin
from ..base import BaseEstimator, RegressorMixin
... | {
"repo_name": "maxlikely/scikit-learn",
"path": "sklearn/linear_model/stochastic_gradient.py",
"copies": "1",
"size": "39157",
"license": "bsd-3-clause",
"hash": -7357379114141780000,
"line_mean": 37.9621890547,
"line_max": 79,
"alpha_frac": 0.5612534157,
"autogenerated": false,
"ratio": 4.219959... |
"""Implementation of Stochastic Gradient Descent (SGD)."""
import numpy as np
import scipy.sparse as sp
import warnings
from ..externals.joblib import Parallel, delayed
from ..base import RegressorMixin
from ..base import ClassifierMixin
from ..feature_selection.selector_mixin import SelectorMixin
from .base import ... | {
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"path": "sklearn/linear_model/stochastic_gradient.py",
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__authors__ = 'Petteri Ponsimaa, Ari Kairala'
import unittest, os, hashlib
import exam_archive
import server
# Path to the database file, different from the deployment db
db_path = 'db/exam_archive_test.db'
db = exam_archive.ExamArchiveDatabase(db_path)
class BaseTestCase(unittest.TestCase):
'''
... | {
"repo_name": "petterip/exam-archive",
"path": "test/database_api_test_common.py",
"copies": "1",
"size": "1477",
"license": "mit",
"hash": 3979898260992422400,
"line_mean": 30.1086956522,
"line_max": 88,
"alpha_frac": 0.6283006093,
"autogenerated": false,
"ratio": 3.949197860962567,
"config_te... |
''' @author: Sphere '''
import xbmcaddon,xbmcgui,xbmc,os,re,sys,urllib,urllib2
from xbmc import getCondVisibility as condtition,translatePath as translate
addon=xbmcaddon.Addon(id='plugin.video.xbmchubmaintenance'); ADDON_TITLE=addon.getAddonInfo('name'); DEBUG=False
STRINGS={'do_upload':30000,'upload_id':30001,'up... | {
"repo_name": "aplicatii-romanesti/allinclusive-kodi-pi",
"path": ".kodi/addons/plugin.video.xbmchubmaintenance/upload.py",
"copies": "1",
"size": "6186",
"license": "apache-2.0",
"hash": 4909086081843018000,
"line_mean": 77.3076923077,
"line_max": 254,
"alpha_frac": 0.653895894,
"autogenerated": f... |
from SingleFileParser import SingleFileParser
from os import listdir
from os.path import isfile, join, isdir, splitext, basename
parsed_classes = []
parsed_functions = []
using_relations_str = []
lib_headers = []
mypath = "../../include/fertilized"
onlysubdirs = ['.'] + [ f for f in listdir(mypath) if isdir(join(mypa... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/ParseHeader.py",
"copies": "1",
"size": "1701",
"license": "bsd-2-clause",
"hash": 9012298978733692000,
"line_mean": 38.5581395349,
"line_max": 82,
"alpha_frac": 0.5720164609,
"autogenerated": false,
"ratio": 3.5961945031712... |
from TemplateParameterParser import *
from WrappedMethodCreator import *
from helper_classes import FertilizedClass, InstantiationTypes, RstDocProvider
import CppHeaderParser
import sys
import os
from DoxygenTypeExtractor import DoxygenTypeExtractor
class SingleFileParser(object):
r"""
Parses a cpp header file... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/SingleFileParser.py",
"copies": "1",
"size": "11211",
"license": "bsd-2-clause",
"hash": 646379672649598100,
"line_mean": 46.5042372881,
"line_max": 114,
"alpha_frac": 0.5557042191,
"autogenerated": false,
"ratio": 4.8012847... |
from TypeTranslations import _dtype_c_translation, _dtype_str_translation,\
_matlab_cpp_translation, _cpp_matlab_translation
from collections import namedtuple
import re
class Node(object):
r"""
A very simple node object for the graph based dependency analysis.
"""
def __init__(self,
content):... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/helper_classes.py",
"copies": "1",
"size": "40650",
"license": "bsd-2-clause",
"hash": -6332629898298690000,
"line_mean": 39.7314629259,
"line_max": 200,
"alpha_frac": 0.5726937269,
"autogenerated": false,
"ratio": 4.0443736... |
import re
from helper_classes import Argument, CppType
def PostprocessDefaultValue(defaultValue):
if not defaultValue == None:
return defaultValue \
.replace("false", "0") \
.replace("true", "1") \
.replace(" ", "") \
.replace("f", "") # 0.1f -> 0... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/ParameterWrapper.py",
"copies": "1",
"size": "7585",
"license": "bsd-2-clause",
"hash": 8744962896906734000,
"line_mean": 45.5337423313,
"line_max": 153,
"alpha_frac": 0.5550428477,
"autogenerated": false,
"ratio": 4.0867456... |
import re
from helper_classes import CppType, WrappedMethod, Argument, RstDocProvider
from ParameterWrapper import ParameterWrapper, parse_raw_type
class WrappedMethodCreator(object):
"""Uses a raw cpp header method (from CppHeaderParser) to create a WrapperMethod object,
which is required to generate the m... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/WrappedMethodCreator.py",
"copies": "1",
"size": "4899",
"license": "bsd-2-clause",
"hash": -6267216670027406000,
"line_mean": 43.1441441441,
"line_max": 119,
"alpha_frac": 0.5180649112,
"autogenerated": false,
"ratio": 5.10... |
import re
from TypeTranslations import _dtype_str_translation
from helper_classes import InstantiationTypes
class DoxygenTypeExtractor(object):
"""Extracts types and available interfaces from a doxygen class comment."""
def __init__(self, doxygenComment):
self.DoxygenComment = doxygenComment
... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/DoxygenTypeExtractor.py",
"copies": "1",
"size": "4679",
"license": "bsd-2-clause",
"hash": -8568160827867649000,
"line_mean": 38.6525423729,
"line_max": 87,
"alpha_frac": 0.577901261,
"autogenerated": false,
"ratio": 4.6977... |
import re
class TemplateParameterParser(object):
"""Parses a template definition of a cpp class into an array of those template parameters"""
def __init__(self, filename, line):
self.Filename = filename
self.LineNo = line
def RetrieveTemplateParameters(self):
templateParamsStr = N... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/TemplateParameterParser.py",
"copies": "1",
"size": "1281",
"license": "bsd-2-clause",
"hash": -704034146320608900,
"line_mean": 34.6111111111,
"line_max": 96,
"alpha_frac": 0.5706479313,
"autogenerated": false,
"ratio": 4.3... |
import jinja2
import os
import shutil
import collections
import operator
import itertools
import glob
import sys
import traceback
from os.path import basename
from helper_classes import InstantiationTypes, Node
from TypeTranslations import _dtype_str_translation
from ordered_set import OrderedSet
BASE_DIR = os.path.j... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/generate.py",
"copies": "1",
"size": "29116",
"license": "bsd-2-clause",
"hash": -6848194631837365000,
"line_mean": 44.7080062794,
"line_max": 165,
"alpha_frac": 0.6564088474,
"autogenerated": false,
"ratio": 3.5377885783718... |
__author__ = 'Spiderman'
import requests
from requests_oauthlib import OAuth1
from random import randint
from time import sleep
from sys import argv, exc_clear, exc_info
#If you want my Twitter OAuth key, shoot me an email at suckmyspiderdick@gmail.com
authkey = OAuth1('')
previousTrend = 'goddamnit python'... | {
"repo_name": "SUCKMYSPIDERDICK/SUCKMYSPIDERDIC-Twitter-Bot",
"path": "twitter_bot.py",
"copies": "1",
"size": "2115",
"license": "mit",
"hash": 6973741424008134000,
"line_mean": 33.25,
"line_max": 129,
"alpha_frac": 0.6671394799,
"autogenerated": false,
"ratio": 3.253846153846154,
"config_test... |
__author__ = 'spousty aka TheSteve0'
import csv
infile_txt = '/home/spousty/data/MOLW/bird/BasicData/ebd_US-CA-087_prv_relNov-2016/ebd_US-CA-087_prv_relNov-2016.txt'
outfile = open('./all_obs.sql', 'w')
first_sql = '''INSERT INTO public.birdobs(global_unique_identifier, taxonomic_order, category,common_name, scientif... | {
"repo_name": "molw/ScriptsAndDDL",
"path": "dbwork/ebird/scripts_orig_data/translator.py",
"copies": "1",
"size": "3513",
"license": "apache-2.0",
"hash": -7768959097908265000,
"line_mean": 42.925,
"line_max": 140,
"alpha_frac": 0.6501565613,
"autogenerated": false,
"ratio": 3.381135707410972,
... |
__author__ = 'spousty aka TheSteve0'
import csv
infile_txt = '/home/spousty/data/MOLW/marine/859583.csv'
outfile = open('./all_obs.sql', 'w')
fields_in_order = ['identification', 'location', 'time_of_observation', 'sea_level_pressure', \
'characteristics_of_pressure_tendency', 'pressure_tendency', 'air_temperature',... | {
"repo_name": "molw/ScriptsAndDDL",
"path": "dbwork/marine/scripts_orig_data/translator.py",
"copies": "1",
"size": "3143",
"license": "apache-2.0",
"hash": 3132758659000657000,
"line_mean": 35.1379310345,
"line_max": 120,
"alpha_frac": 0.6261533567,
"autogenerated": false,
"ratio": 3.25362318840... |
__author__ = 'spousty'
import psycopg2
from bottle import route, run, get, DEBUG
import os
@route('/')
def index():
return "<h1> hello OpenShift Ninja without DB</h1>"
# since this is a read only talk to the replicas
@get('/db')
def dbexample():
try:
conn = psycopg2.connect(database=os.environ.get(... | {
"repo_name": "thesteve0/v3simple-spatial",
"path": "2_app.py",
"copies": "1",
"size": "1114",
"license": "apache-2.0",
"hash": -8203884455535503000,
"line_mean": 28.3157894737,
"line_max": 116,
"alpha_frac": 0.6068222621,
"autogenerated": false,
"ratio": 3.164772727272727,
"config_test": false... |
__author__ = 'spousty'
import psycopg2
from bottle import route, run, get, post, DEBUG
import os
import random
from random_words import RandomWords
@route('/')
def index():
return "<h1>Hello OpenShift Ninja without a DB!</h1>"
# since this is a read only talk to the replicas
@get('/db')
def dbexample():
tr... | {
"repo_name": "thesteve0/v3simple-spatial",
"path": "app.py",
"copies": "1",
"size": "2521",
"license": "apache-2.0",
"hash": -3752425116580868600,
"line_mean": 31.7402597403,
"line_max": 116,
"alpha_frac": 0.606505355,
"autogenerated": false,
"ratio": 3.286831812255541,
"config_test": false,
... |
__author__ = 'spousty'
import psycopg2
from bottle import route, run, get, static_file, DEBUG
import os,json
@route('/')
def index():
return static_file("index.html", root='./')
@get('/ws/zips')
def getzips():
results = []
try:
conn = psycopg2.connect(database=os.environ.get('POSTGRES_DB'), u... | {
"repo_name": "thesteve0/awsdemo",
"path": "app.py",
"copies": "1",
"size": "3536",
"license": "apache-2.0",
"hash": -5644042837866656000,
"line_mean": 29.4827586207,
"line_max": 115,
"alpha_frac": 0.6001131222,
"autogenerated": false,
"ratio": 3.2650046168051707,
"config_test": false,
"has_n... |
import urllib2
import json
import demjson
k=0
while True:
#####################bot379578526:AAGDRqKr3d2N8uWj6sHuBC3fS8_mOaEpqkk in the below link have to be raplaced by your bot's token
#Gets the data from the user in JSON Format
a=urllib2.urlopen('https://api.telegram.org/bot379578526:AAGDRqKr3d2N8uWj6sH... | {
"repo_name": "PradeepNalluri/News-Bot-in-Telegram",
"path": "Telegram News Bot.py",
"copies": "1",
"size": "3002",
"license": "apache-2.0",
"hash": 2766737992589900300,
"line_mean": 53.5818181818,
"line_max": 194,
"alpha_frac": 0.6329113924,
"autogenerated": false,
"ratio": 2.658990256864482,
... |
__author__ = 'Spyridon Samothrakis ssamot@essex.ac.uk'
import numpy as np
# shamelessly stollen from wikipedia
def int2bin(n):
'From positive integer to list of binary bits, msb at index 0'
if n:
bits = []
while n:
n,remainder = divmod(n, 2)
bits.insert(0, remainder)
return bits
else: return [0]
def... | {
"repo_name": "ssamot/infoGA",
"path": "pbil.py",
"copies": "1",
"size": "3681",
"license": "apache-2.0",
"hash": 5668294995945563000,
"line_mean": 25.6739130435,
"line_max": 106,
"alpha_frac": 0.5536538984,
"autogenerated": false,
"ratio": 3.0573089700996676,
"config_test": false,
"has_no_ke... |
__author__ = 'squiresrb'
def parse_blast_result(blast_file_path):
from Bio.Blast import NCBIXML
output_file = "%s.alignments.txt" % blast_file_path
output_handle = open(output_file, 'w')
result_handle = open(blast_file_path)
blast_records = NCBIXML.parse(result_handle)
#E_VALUE_THRESH = 0.04
... | {
"repo_name": "parasite-genomics/Pipelines",
"path": "blast_xml_to_text.py",
"copies": "1",
"size": "2170",
"license": "apache-2.0",
"hash": 5601193782145786000,
"line_mean": 47.2222222222,
"line_max": 124,
"alpha_frac": 0.5857142857,
"autogenerated": false,
"ratio": 3.4887459807073955,
"config... |
import sys
import pickle
from sklearn.utils.deprecation import _is_deprecated
from sklearn.utils.deprecation import deprecated
from sklearn.utils.testing import assert_warns_message
from sklearn.utils.testing import assert_no_warnings
from sklearn.utils.testing import SkipTest
from sklearn.utils.deprecation import D... | {
"repo_name": "herilalaina/scikit-learn",
"path": "sklearn/utils/tests/test_deprecation.py",
"copies": "25",
"size": "2158",
"license": "bsd-3-clause",
"hash": 2588762470472899600,
"line_mean": 26.6666666667,
"line_max": 79,
"alpha_frac": 0.6816496756,
"autogenerated": false,
"ratio": 3.785964912... |
__author__ = 'sravi'
from flask.ext.restful import Resource, reqparse, abort
from datetime import timedelta
from offload import async, sched_in, get_tasks_scheduled
from qq.server import auth
from qq.tasks.task import count_words_at_url, query_postgres
class HelloWorld(Resource):
def get(self):
task = as... | {
"repo_name": "codeforgood/qq",
"path": "server/qq/views/views.py",
"copies": "1",
"size": "1193",
"license": "mit",
"hash": 7066738437991489000,
"line_mean": 28.825,
"line_max": 106,
"alpha_frac": 0.667225482,
"autogenerated": false,
"ratio": 3.5825825825825826,
"config_test": false,
"has_no... |
__author__ = 'sravi'
import os
from flask import Flask
from flask_environments import Environments
from flask.ext import restful
from flask.ext.bcrypt import Bcrypt
from flask.ext.httpauth import HTTPBasicAuth
from redis import Redis
from rq import Queue
from rq_dashboard import RQDashboard
from rq_scheduler import S... | {
"repo_name": "codeforgood/qq",
"path": "server/qq/server.py",
"copies": "1",
"size": "1219",
"license": "mit",
"hash": -7956013461265504000,
"line_mean": 21.5740740741,
"line_max": 86,
"alpha_frac": 0.724364233,
"autogenerated": false,
"ratio": 3.2334217506631298,
"config_test": false,
"has_... |
__author__ = 'sravi'
import unittest
import api
import json
class MultiCasterFunctionalTest(unittest.TestCase):
def setUp(self):
api.app.config['TESTING'] = True
self.app = api.app.test_client()
def tearDown(self):
pass
def test_api_valid_single_recipient(self):
data = ... | {
"repo_name": "codeforgood/muticaster",
"path": "tests/functional_test_multicaster.py",
"copies": "1",
"size": "10975",
"license": "unlicense",
"hash": -1922805001728501500,
"line_mean": 49.8148148148,
"line_max": 91,
"alpha_frac": 0.517904328,
"autogenerated": false,
"ratio": 3.592471358428805,
... |
__author__ = 'sravi'
# Relays dict as represented in the problem description
relays_dict = {
"S": {"name": "Small", "throughput": 1, "cost": 0.01, "subnet": "10.0.1.0/24"},
"M": {"name": "Medium", "throughput": 5, "cost": 0.05, "subnet": "10.0.2.0/24"},
"L": {"name": "Large", "throughput": 10, "cost": 0.10... | {
"repo_name": "codeforgood/muticaster",
"path": "api/multicastservice.py",
"copies": "1",
"size": "2576",
"license": "unlicense",
"hash": -8630372094048080000,
"line_mean": 39.8888888889,
"line_max": 96,
"alpha_frac": 0.6013198758,
"autogenerated": false,
"ratio": 3.4209827357237717,
"config_te... |
__author__ = 'sreeder'
from ...ODM2.models import *
from .. import serviceBase
import datetime as dt
import uuid
class CreateODM2( serviceBase):
'''
def __init__(self, session):
self._session = session
'''
# ################################################################################
# Annotat... | {
"repo_name": "Castronova/ODM2PythonAPI",
"path": "src/api/ODM2/services/createService.py",
"copies": "1",
"size": "21123",
"license": "bsd-3-clause",
"hash": 1359754424817506000,
"line_mean": 29.5687409551,
"line_max": 127,
"alpha_frac": 0.523883918,
"autogenerated": false,
"ratio": 4.9514767932... |
"""
Routines that wrap AmberTools programs
This module defines two public functions:
run_antechamber
run_parmchk
Can be executed from the command line as a stand-alone program
Modified by Samuel Genheden
"""
import os
import subprocess
import tempfile
def run_program ( name, command ):
"""
Wrapper... | {
"repo_name": "SGenheden/Scripts",
"path": "sgenlib/ambertools.py",
"copies": "1",
"size": "3248",
"license": "mit",
"hash": 8281051604900640000,
"line_mean": 25.6229508197,
"line_max": 123,
"alpha_frac": 0.6200738916,
"autogenerated": false,
"ratio": 3.67836919592299,
"config_test": false,
"... |
import csv
import pdb
import math
import sys
import pydot
import random
def cal_Entropy(a,b):
a = float(a)
b = float(b)
if a==0 and b==0:
return -1
pa = a/(a+b)
pb = b/(a+b)
if pa == 0:
E = -pb*math.log(pb,2)
return E
elif pb == 0:
E = -pa*math.log(pa,2)
... | {
"repo_name": "ganjash/machine-learning",
"path": "Decision tree/tree.py",
"copies": "1",
"size": "13740",
"license": "mit",
"hash": -8657038118000542000,
"line_mean": 30.5862068966,
"line_max": 112,
"alpha_frac": 0.4558951965,
"autogenerated": false,
"ratio": 3.2367491166077738,
"config_test":... |
import re
import os
try:
from urllib.request import urlopen
from urllib.parse import quote
except ImportError:
from urllib import urlopen
from urllib import quote
# Compatibility functions
# Check for existence of builtin function next()
try:
next
except NameError:
def next(it):
retur... | {
"repo_name": "NcLang/vimrc",
"path": "sources_non_forked/vim-latex/ftplugin/latex-suite/bibtools.py",
"copies": "1",
"size": "7716",
"license": "mit",
"hash": -8008951495224221000,
"line_mean": 29.9879518072,
"line_max": 119,
"alpha_frac": 0.4489372732,
"autogenerated": false,
"ratio": 3.9528688... |
import re
import urllib
class Bibliography(dict):
def __init__(self, txt, macros={}):
"""
txt:
a string which represents the entire bibtex entry. A typical
entry is of the form:
@ARTICLE{ellington:84:part3,
author = {Ellington, C P},
... | {
"repo_name": "wathen/dotfiles",
"path": ".vim/bundle/vim-latex-1.8.23-20141116.812-gitd0f31c9/ftplugin/latex-suite/bibtools.py",
"copies": "6",
"size": "7083",
"license": "mit",
"hash": -1128084705697837400,
"line_mean": 30.6205357143,
"line_max": 119,
"alpha_frac": 0.4270789214,
"autogenerated": ... |
import re
class Bibliography(dict):
def __init__(self, txt, macros={}):
"""
txt:
a string which represents the entire bibtex entry. A typical
entry is of the form:
@ARTICLE{ellington:84:part3,
author = {Ellington, C P},
ti... | {
"repo_name": "ppslinux/Vundle.vim",
"path": "myvim/ftplugin/latex-suite/bibtools.py",
"copies": "3",
"size": "7030",
"license": "mit",
"hash": -1777330411400799200,
"line_mean": 30.8099547511,
"line_max": 119,
"alpha_frac": 0.426458037,
"autogenerated": false,
"ratio": 3.949438202247191,
"conf... |
import re
class Bibliography(dict):
def __init__(self, txt, macros={}):
"""
txt:
a string which represents the entire bibtex entry. A typical
entry is of the form:
@ARTICLE{ellington:84:part3,
author = {Ellington, C P},
... | {
"repo_name": "nagahar/dotfiles",
"path": ".vim/bundle/nosync/vim-latex/ftplugin/latex-suite/bibtools.py",
"copies": "1",
"size": "7251",
"license": "mit",
"hash": 6358457038260177000,
"line_mean": 30.8099547511,
"line_max": 119,
"alpha_frac": 0.4134602124,
"autogenerated": false,
"ratio": 4.0463... |
__author__ = 'srio'
# https://stackoverflow.com/questions/21566379/fitting-a-2d-gaussian-function-using-scipy-optimize-curve-fit-valueerror-and-m
import numpy as np
from srxraylib.plot.gol import plot_image
import scipy.optimize as opt
def fit_gaussian2d(data,x0,y0,p0=None):
if p0 is None:
p0 = moments(da... | {
"repo_name": "srio/Orange-XOPPY",
"path": "orangecontrib/xoppy/util/fit_gaussian2d.py",
"copies": "1",
"size": "3473",
"license": "bsd-2-clause",
"hash": -7049742763899613000,
"line_mean": 31.7641509434,
"line_max": 125,
"alpha_frac": 0.5096458393,
"autogenerated": false,
"ratio": 2.415159944367... |
__author__ = 'srio'
import sys
import numpy
import scipy.constants as codata
from oasys.widgets import widget
from orangewidget import gui
from PyQt4 import QtGui
from crystalpy.util.PolarizedPhotonBunch import PolarizedPhotonBunch
from crystalpy.util.PolarizedPhoton import PolarizedPhoton
from crystalpy.util.Vecto... | {
"repo_name": "edocappelli/oasys-crystalpy",
"path": "orangecontrib/oasyscrystalpy/widgets/elements/ShadowConverter.py",
"copies": "1",
"size": "7933",
"license": "mit",
"hash": -8884561568206631000,
"line_mean": 33.1939655172,
"line_max": 137,
"alpha_frac": 0.5584268247,
"autogenerated": false,
... |
__author__ = 'srio'
import json
import numpy
from gfile import GFile
class OE(object):
def __init__(self):
self.FMIRR = 5
self.F_TORUS = 0
self.FCYL = 0
self.F_EXT = 0
self.FSTAT = 0
self.F_SCREEN = 0
self.F_PLATE = 0
self.FSLIT = 0
self.F... | {
"repo_name": "srio/minishadow",
"path": "minishadow/io/OE.py",
"copies": "1",
"size": "13465",
"license": "mit",
"hash": -2573405794111193000,
"line_mean": 31.3677884615,
"line_max": 67,
"alpha_frac": 0.5484589677,
"autogenerated": false,
"ratio": 3.0658014571948997,
"config_test": false,
"h... |
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