text stringlengths 0 1.05M | meta dict |
|---|---|
from faps.sibshipCluster import sibshipCluster
import numpy as np
import pandas as pd
import faps as fp
ndraws=1000
np.random.seed(867)
allele_freqs = np.random.uniform(0.3,0.5,50)
adults = fp.make_parents(100, allele_freqs, family_name='a')
def test_sires():
# Example with a single family
progeny = fp.make_s... | {
"repo_name": "ellisztamas/faps",
"path": "tests/test_sires.py",
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"size": "1472",
"license": "mit",
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"line_mean": 39.9166666667,
"line_max": 110,
"alpha_frac": 0.6908967391,
"autogenerated": false,
"ratio": 2.507666098807496,
"config_test": false,
"ha... |
from faps.sibshipCluster import sibshipCluster
import numpy as np
import pandas as pd
import faps as fp
ndraws=1000
np.random.seed(867)
def test_method():
# Simulate a starting population
allele_freqs = np.random.uniform(0.3,0.5,50)
adults = fp.make_parents(100, allele_freqs, family_name='a')
progeny ... | {
"repo_name": "ellisztamas/faps",
"path": "tests/test_simulate_mating.py",
"copies": "1",
"size": "2331",
"license": "mit",
"hash": 8786748343620907000,
"line_mean": 39.1896551724,
"line_max": 106,
"alpha_frac": 0.7104247104,
"autogenerated": false,
"ratio": 2.8050541516245486,
"config_test": f... |
from farmfs.fs import normpath as _normalize
from farmfs.fs import userPath2Path as up2p
from farmfs.fs import Path, FileDoesNotExist, InvalidArgument, NotPermitted, FileExists, IsADirectory, ensure_symlink, ensure_absent, ensure_dir, ensure_link, ensure_copy, ensure_file
from farmfs.fs import XSym
import pytest
import... | {
"repo_name": "andrewguy9/farmfs",
"path": "tests/test_fs.py",
"copies": "1",
"size": "17925",
"license": "mit",
"hash": 6413298873402238000,
"line_mean": 28.0518638574,
"line_max": 183,
"alpha_frac": 0.5859972106,
"autogenerated": false,
"ratio": 2.9117933723196883,
"config_test": true,
"has... |
from farmfs.fs import Path, ensure_link, ensure_readonly, ensure_symlink, ensure_copy, ftype_selector, FILE, is_readonly
from func_prototypes import typed, returned
from farmfs.util import safetype, pipeline, fmap, first, compose, invert, partial, repeater
from os.path import sep
from s3lib import Connection as s3conn,... | {
"repo_name": "andrewguy9/farmfs",
"path": "farmfs/blobstore.py",
"copies": "1",
"size": "5758",
"license": "mit",
"hash": 6995617793431177000,
"line_mean": 36.3896103896,
"line_max": 120,
"alpha_frac": 0.6120180618,
"autogenerated": false,
"ratio": 3.6721938775510203,
"config_test": false,
"... |
from farmfs.fs import Path, LINK, DIR, FILE, ingest, ROOT
from func_prototypes import typed
from delnone import delnone
from os.path import sep
from functools import total_ordering
from farmfs.util import safetype
from future.utils import python_2_unicode_compatible
try:
from itertools import imap
except ImportErr... | {
"repo_name": "andrewguy9/farmfs",
"path": "farmfs/snapshot.py",
"copies": "1",
"size": "5198",
"license": "mit",
"hash": 7012039922441606000,
"line_mean": 29.9404761905,
"line_max": 118,
"alpha_frac": 0.6298576376,
"autogenerated": false,
"ratio": 3.5774260151410875,
"config_test": false,
"h... |
from farmfs.fs import Path
from farmfs.fs import ensure_dir
from farmfs.fs import ensure_file
from farmfs.fs import Path
from hashlib import md5
from json import loads, JSONEncoder
from errno import ENOENT as NoSuchFile
from errno import EISDIR as IsDirectory
from os.path import sep
from func_prototypes import typed, r... | {
"repo_name": "andrewguy9/farmfs",
"path": "farmfs/keydb.py",
"copies": "1",
"size": "3509",
"license": "mit",
"hash": 9112114885381147000,
"line_mean": 27.762295082,
"line_max": 126,
"alpha_frac": 0.6554573953,
"autogenerated": false,
"ratio": 3.27027027027027,
"config_test": false,
"has_no_... |
from farmfs.fs import sep, ROOT, Path, LINK, DIR
from itertools import permutations, combinations, chain, product
from collections import defaultdict
def permute_deep(options):
options = [permutations(options, pick) for pick in range(1,1+len(options))]
return list(chain.from_iterable(options))
def combine_dee... | {
"repo_name": "andrewguy9/farmfs",
"path": "tests/trees.py",
"copies": "1",
"size": "2663",
"license": "mit",
"hash": -800377577328301400,
"line_mean": 30.3294117647,
"line_max": 97,
"alpha_frac": 0.6920766053,
"autogenerated": false,
"ratio": 3.4629388816644995,
"config_test": false,
"has_no... |
from farmfs.transduce import arrayOf, sumOf, joinedWith, map, filter, compose, reduceWith, transduce
def test_accumulators():
acc = [1,2,3]
val = 4
reducer = lambda acc, x: acc + [x] or acc
assert reducer(acc, val) == [1,2,3,4]
acc = set([1,2,3])
val = 4
reducer = lambda acc, x: acc.add(x) ... | {
"repo_name": "andrewguy9/farmfs",
"path": "tests/test_transduce.py",
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"size": "3286",
"license": "mit",
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"line_mean": 37.2093023256,
"line_max": 100,
"alpha_frac": 0.6503347535,
"autogenerated": false,
"ratio": 2.842560553633218,
"config_test": true,
... |
from farmfs.volume import mkfs as make_volume
from farmfs.volume import FarmFSVolume
from farmfs.fs import Path
from farmfs.keydb import KeyDBWindow
from func_prototypes import typed, returned
from farmfs.util import take, ingest
try:
from os import getcwdu
getcwd_utf = lambda : ingest(getcwdu())
except ImportE... | {
"repo_name": "andrewguy9/farmfs",
"path": "farmfs/__init__.py",
"copies": "1",
"size": "1165",
"license": "mit",
"hash": -4738400819054615000,
"line_mean": 24.8888888889,
"line_max": 64,
"alpha_frac": 0.7107296137,
"autogenerated": false,
"ratio": 3.2724719101123596,
"config_test": false,
"h... |
from farms.models import *
from common.utils import daterange
from farms.generate_water_register import generate_water_register
from django.forms.models import modelformset_factory
from django.db.models import Q
from datetime import datetime, date, timedelta
from irrigator_pro.settings import WATER_REGISTER_DELTA
... | {
"repo_name": "warnes/irrigatorpro",
"path": "irrigator_pro/farms/unified_field_data.py",
"copies": "1",
"size": "6393",
"license": "mit",
"hash": -3589539645017265700,
"line_mean": 35.9537572254,
"line_max": 108,
"alpha_frac": 0.6012826529,
"autogenerated": false,
"ratio": 4.087595907928389,
"... |
from fashion.databaseAccess import DatabaseAccess
from fashion.modelAccess import ModelAccess
from fashion.portfolio import FASHION_WAREHOUSE_PATH
from fashion.runway import Runway
from fashion.schema import SchemaRepository
from fashion.warehouse import Warehouse
class DummyXform(object):
def __init__(self, mdb,... | {
"repo_name": "braddillman/fashion",
"path": "test/test_runway.py",
"copies": "1",
"size": "3240",
"license": "apache-2.0",
"hash": -6378142002020358000,
"line_mean": 30.7647058824,
"line_max": 54,
"alpha_frac": 0.5583333333,
"autogenerated": false,
"ratio": 3.319672131147541,
"config_test": tr... |
from fasta.Fasta import read_fasta
from collections import defaultdict
visited = defaultdict(list)
def node_exists(left, right):
return (left[2] in visited.keys() and right[1] in visited.values()) or (right[2] in visited.keys() and left[1] in visited.values())
if __name__ == '__main__':
data = '''
>Rosalind_... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/grph.py",
"copies": "1",
"size": "11759",
"license": "unlicense",
"hash": 3823458682084885500,
"line_mean": 33.7928994083,
"line_max": 135,
"alpha_frac": 0.9173399099,
"autogenerated": false,
"ratio": 2.3079489695780175,
"config_test": fals... |
from fasta.Fasta import read_fasta
from urllib import urlopen
import re
#damn regexes
le_regex_1 = '[N][^P][T][^P]'
le_regex_2 = '[N][^P][S][^P]'
if __name__ == '__main__':
data = '''
P02725_GLP_PIG
P19835_BAL_HUMAN
O13188
P02760_HC_HUMAN
Q4FZD7
Q00001_RHGA_ASPAC
Q47A87
Q8R1Y2
A6NM15
P22457_FA7_BOVIN
P03415_VME1_... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/mprt.py",
"copies": "1",
"size": "1176",
"license": "unlicense",
"hash": 3176792088946329600,
"line_mean": 24.0212765957,
"line_max": 64,
"alpha_frac": 0.537414966,
"autogenerated": false,
"ratio": 3.0153846153846153,
"config_test": false,
... |
from fasta.Fasta import read_fasta
import decimal
context = decimal.getcontext()
context.prec = 12
context.rounding = decimal.ROUND_HALF_DOWN
def is_transition(orig, found):
if (orig == 'A' and found == 'G'):
return True
if (orig == 'G' and found == 'A'):
return True
if (orig == 'C' and fo... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/tran.py",
"copies": "1",
"size": "3356",
"license": "unlicense",
"hash": 8795518779956960000,
"line_mean": 37.1477272727,
"line_max": 88,
"alpha_frac": 0.7935041716,
"autogenerated": false,
"ratio": 2.766694146743611,
"config_test": false,
... |
from fasta.Fasta import read_fasta
import sys
if __name__ == '__main__':
data = '''
>Rosalind_4592
>Rosalind_9829
TTAGACCATGCTGTTGTACTCCCCCCGTCATGGCAAAAATGACTCATTCGAGTCTTTCGC
ATGCGTCCACCCGGCTGTGGACTTGTCTGTTCGGCCTAGGCGTGACAAAGGTTAAAGTCA
TGTATACAGGATGCCGACCAAATGTAGAGCTACTCATTCCGTATAGCTTCTAAGCCACTA
ACGAGGATACGAAGTGTA... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/cons.py",
"copies": "1",
"size": "10762",
"license": "unlicense",
"hash": 3255450500082523600,
"line_mean": 45.5930735931,
"line_max": 62,
"alpha_frac": 0.8982531128,
"autogenerated": false,
"ratio": 2.475730388773867,
"config_test": false,... |
from fasta.Fasta import read_fasta
import time
#http://code.activestate.com/recipes/286222/
import os
_proc_status = '/proc/%d/status' % os.getpid()
_scale = {'kB': 1024.0, 'mB': 1024.0*1024.0,
'KB': 1024.0, 'MB': 1024.0*1024.0}
def _VmB(VmKey):
'''Private.
'''
global _proc_status, _scale
# get pseudo file ... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/mmch.py",
"copies": "1",
"size": "1565",
"license": "unlicense",
"hash": 955775624299243800,
"line_mean": 19.3246753247,
"line_max": 74,
"alpha_frac": 0.6217252396,
"autogenerated": false,
"ratio": 2.5201288244766507,
"config_test": false,
... |
from fasta.Fasta import read_fasta
# http://rosettacode.org/wiki/Longest_common_subsequence#Python
def lcs(a, b):
lengths = [[0 for j in range(len(b)+1)] for i in range(len(a)+1)]
# row 0 and column 0 are initialized to 0 already
for i, x in enumerate(a):
for j, y in enumerate(b):
if x ... | {
"repo_name": "kinow/rosalind-exercises",
"path": "src/lcsq.py",
"copies": "1",
"size": "3098",
"license": "unlicense",
"hash": -2398837430389698600,
"line_mean": 41.4520547945,
"line_max": 69,
"alpha_frac": 0.8085861846,
"autogenerated": false,
"ratio": 2.5752285951787197,
"config_test": false... |
from fastai2.basics import *
from fastai2.vision.all import *
from fastai2.callback.all import *
from fastai2.distributed import *
from fastprogress import fastprogress
from torchvision.models import *
from fastai2.vision.models.xresnet import *
from fastai2.callback.mixup import *
from fastscript import *
torch.backe... | {
"repo_name": "hfp/libxsmm",
"path": "samples/deeplearning/sparse_training/resnet/train_imagenette.py",
"copies": "1",
"size": "4041",
"license": "bsd-3-clause",
"hash": -2782945121278682000,
"line_mean": 45.988372093,
"line_max": 112,
"alpha_frac": 0.6374659738,
"autogenerated": false,
"ratio": ... |
from fastai.basics import *
from fastai.callback.all import *
from fastai.distributed import *
from fastprogress import fastprogress
from fastai.callback.mixup import *
from fastcore.script import *
from fastai.text.all import *
torch.backends.cudnn.benchmark = True
fastprogress.MAX_COLS = 80
def pr(s):
if rank_di... | {
"repo_name": "fastai/fastai",
"path": "nbs/examples/train_imdbclassifier.py",
"copies": "1",
"size": "1574",
"license": "apache-2.0",
"hash": 4505841680408128500,
"line_mean": 38.35,
"line_max": 109,
"alpha_frac": 0.6728081321,
"autogenerated": false,
"ratio": 3.3347457627118646,
"config_test"... |
from fastai.basics import *
from fastai.tabular.all import *
from fastai.callback.all import *
from fastai.distributed import *
from fastprogress import fastprogress
from fastai.callback.mixup import *
from fastcore.script import *
torch.backends.cudnn.benchmark = True
fastprogress.MAX_COLS = 80
def pr(s):
if ran... | {
"repo_name": "fastai/fastai",
"path": "nbs/examples/train_tabular.py",
"copies": "1",
"size": "1471",
"license": "apache-2.0",
"hash": 2835621037046062000,
"line_mean": 33.2093023256,
"line_max": 84,
"alpha_frac": 0.6532970768,
"autogenerated": false,
"ratio": 3.232967032967033,
"config_test":... |
from fastai.basics import *
from fastai.text.all import *
from fastai.callback.all import *
from fastcore.script import *
def istitle(line):
return len(re.findall(r'^ = [^=]* = $', line)) != 0
def read_file(filename):
articles = L()
with open(filename, encoding='utf8') as f:
lines = f.readlines()
... | {
"repo_name": "fastai/fastai",
"path": "nbs/examples/train_wt2.py",
"copies": "1",
"size": "2023",
"license": "apache-2.0",
"hash": 21303931171258936,
"line_mean": 42.9782608696,
"line_max": 127,
"alpha_frac": 0.6465645082,
"autogenerated": false,
"ratio": 2.817548746518106,
"config_test": fals... |
from fastai.basics import *
from fastai.vision.all import *
from fastai.callback.all import *
from fastai.distributed import *
from fastprogress import fastprogress
from torchvision.models import *
from fastai.vision.models.xresnet import *
from fastai.callback.mixup import *
from fastcore.script import *
torch.backen... | {
"repo_name": "fastai/fastai",
"path": "nbs/examples/train_imagenette.py",
"copies": "1",
"size": "4046",
"license": "apache-2.0",
"hash": 1704497623106431200,
"line_mean": 46.6,
"line_max": 117,
"alpha_frac": 0.6416213544,
"autogenerated": false,
"ratio": 2.9619326500732064,
"config_test": fal... |
from fastai.imports import *
from fastai.transforms import *
from fastai.dataset import *
from sklearn.metrics import fbeta_score
import warnings
def f2(preds, targs, start=0.17, end=0.24, step=0.01):
with warnings.catch_warnings():
warnings.simplefilter("ignore")
return max([fbeta_score(targs, (pr... | {
"repo_name": "jmhsi/justin_tinker",
"path": "data_science/courses/temp/courses/dl1/planet.py",
"copies": "1",
"size": "1352",
"license": "apache-2.0",
"hash": -186423999774780700,
"line_mean": 42.6129032258,
"line_max": 97,
"alpha_frac": 0.6397928994,
"autogenerated": false,
"ratio": 2.781893004... |
from fastai.vision.all import *
from torchvision import datasets, transforms
class Net(nn.Sequential):
def __init__(self):
super().__init__(
nn.Conv2d(1, 32, 3, 1), nn.ReLU(),
nn.Conv2d(32, 64, 3, 1), nn.MaxPool2d(2), nn.Dropout2d(0.25),
Flatten(), nn.Linear(9216, 128), ... | {
"repo_name": "fastai/fastai",
"path": "nbs/examples/migrating_fastai.py",
"copies": "1",
"size": "1183",
"license": "apache-2.0",
"hash": 1042377244250928000,
"line_mean": 39.7931034483,
"line_max": 87,
"alpha_frac": 0.6145393068,
"autogenerated": false,
"ratio": 3.188679245283019,
"config_tes... |
from fastapi import APIRouter, Depends
from . import health, networking, control, settings, deck_calibration, \
modules, pipettes, motors, camera, logs, rpc
from ...dependencies import verify_hardware
legacy_routes = APIRouter()
legacy_routes.include_router(router=health.router,
tags... | {
"repo_name": "Opentrons/labware",
"path": "robot-server/robot_server/service/legacy/routers/__init__.py",
"copies": "2",
"size": "1893",
"license": "apache-2.0",
"hash": -2449482527440101400,
"line_mean": 48.8157894737,
"line_max": 72,
"alpha_frac": 0.5573164289,
"autogenerated": false,
"ratio":... |
from fastapi import APIRouter
from httprunner.runner import HttpRunner
from httprunner.models import ProjectMeta, TestCase
router = APIRouter()
runner = HttpRunner()
@router.post("/hrun/debug/testcase", tags=["debug"])
async def debug_single_testcase(project_meta: ProjectMeta, testcase: TestCase):
resp = {"code... | {
"repo_name": "debugtalk/ApiTestEngine",
"path": "httprunner/app/routers/debug.py",
"copies": "1",
"size": "1565",
"license": "mit",
"hash": -7809796529977316000,
"line_mean": 27.9814814815,
"line_max": 86,
"alpha_frac": 0.6236421725,
"autogenerated": false,
"ratio": 3.3801295896328294,
"config... |
from fastapi import APIRouter, status
from fastapi.responses import JSONResponse
from starlette.responses import RedirectResponse
from mysql_autoxtrabackup.backup_backup.backuper import Backup
from mysql_autoxtrabackup.backup_prepare.prepare import Prepare
from mysql_autoxtrabackup.utils.helpers import list_available_... | {
"repo_name": "ShahriyarR/MySQL-AutoXtraBackup",
"path": "mysql_autoxtrabackup/api/controller/controller.py",
"copies": "1",
"size": "2957",
"license": "mit",
"hash": -2230616605757553700,
"line_mean": 29.4845360825,
"line_max": 88,
"alpha_frac": 0.6706121069,
"autogenerated": false,
"ratio": 3.9... |
from fastapi import Depends, FastAPI, HTTPException
from fastapi.security import OAuth2PasswordRequestForm
from sqlalchemy.orm import Session
import uvicorn
import logging
from api.admin import apirouter
from utils.database import SessionLocal, engine, get_db
from model.token import Token
from model import db
from uti... | {
"repo_name": "yafraorg/yapki",
"path": "server-admin/server.py",
"copies": "1",
"size": "1436",
"license": "apache-2.0",
"hash": 3366892014872372700,
"line_mean": 28.306122449,
"line_max": 86,
"alpha_frac": 0.7151810585,
"autogenerated": false,
"ratio": 3.598997493734336,
"config_test": false,... |
from fastapi import FastAPI, HTTPException, APIRouter
from honeybadger import honeybadger, contrib
import pydantic
honeybadger.configure(api_key='<your-api-key>')
app = FastAPI(title="Honeybadger - FastAPI with Custom Route.")
app.router.route_class = contrib.HoneybadgerRoute
@app.get("/raise_some_error", tags=["Not... | {
"repo_name": "honeybadger-io/honeybadger-python",
"path": "examples/fastapi/custom_route.py",
"copies": "1",
"size": "1216",
"license": "mit",
"hash": 3350822868879705000,
"line_mean": 32.7777777778,
"line_max": 75,
"alpha_frac": 0.7055921053,
"autogenerated": false,
"ratio": 3.3133514986376023,... |
from fastapi import FastAPI
import logging
import sys
from starlette.middleware.cors import CORSMiddleware
from starlette.responses import RedirectResponse
from starlette.staticfiles import StaticFiles
import uvicorn
from piecewise.api.v1 import get_api_router
from piecewise.config.settings import get_settings
# from... | {
"repo_name": "critzo/piecewise",
"path": "backend/piecewise/__main__.py",
"copies": "1",
"size": "1896",
"license": "apache-2.0",
"hash": -5198196280488412000,
"line_mean": 24.28,
"line_max": 80,
"alpha_frac": 0.6587552743,
"autogenerated": false,
"ratio": 3.577358490566038,
"config_test": fal... |
from fastapp.models import Base
__author__ = 'fatrix'
from django.http import HttpResponseRedirect
from django.conf import settings
from re import compile
EXEMPT_URLS = [compile(settings.LOGIN_URL.lstrip('/'))]
if hasattr(settings, 'LOGIN_EXEMPT_URLS'):
EXEMPT_URLS += [compile(expr) for expr in settings.LOGIN_EXE... | {
"repo_name": "sahlinet/fastapp",
"path": "fastapp/middleware.py",
"copies": "1",
"size": "1235",
"license": "mit",
"hash": 6814514527480450000,
"line_mean": 43.1071428571,
"line_max": 83,
"alpha_frac": 0.6979757085,
"autogenerated": false,
"ratio": 4.102990033222591,
"config_test": false,
"h... |
from fastareader import FastaReader
from peptide import Peptide
from xlink import XLink
from utility import *
import bisect
import re
class EnumIndexBuilder:
def __init__(self, fasta_filename, spec_dict, mass, param):
self.fasta_filename = fasta_filename
self.param = param
self.spec_dict = spec_dict
self.un... | {
"repo_name": "COL-IU/XLSearch",
"path": "library/index.py",
"copies": "1",
"size": "4825",
"license": "mit",
"hash": 6759701728271579000,
"line_mean": 30.9536423841,
"line_max": 92,
"alpha_frac": 0.6429015544,
"autogenerated": false,
"ratio": 2.5542615140285867,
"config_test": false,
"has_no... |
from FastaReader import *
from Peptide import *
from XLink import *
from Utility import *
#from multiprocessing.dummy import Pool as ThreadPool
import bisect
import re
class EnumIndexBuilder:
def __init__(self, fastaFileName, spectraDict, mass, param):
self.fastaFileName = fastaFileName
self.param = param
self... | {
"repo_name": "COL-IU/XLSearch",
"path": "library/EnumIndexBuilder.py",
"copies": "1",
"size": "3055",
"license": "mit",
"hash": -3501234854103409700,
"line_mean": 26.2767857143,
"line_max": 86,
"alpha_frac": 0.7001636661,
"autogenerated": false,
"ratio": 2.805325987144169,
"config_test": false... |
from fastcluster import linkage
from scipy.spatial.distance import pdist
from scipy.cluster.hierarchy import fcluster
import pandas as pd
import numpy as np
def profiles_to_np_array(profiles_csv_path):
"""
"""
df = pd.read_csv(profiles_csv_path, index_col=0)
arr = np.array(df, dtype=np.float64)
g... | {
"repo_name": "peterk87/sistr_cmd",
"path": "sistr/src/cgmlst/extras/hclust_cutree.py",
"copies": "1",
"size": "2176",
"license": "apache-2.0",
"hash": -39288133819019016,
"line_mean": 27.6447368421,
"line_max": 101,
"alpha_frac": 0.6475183824,
"autogenerated": false,
"ratio": 3.6206322795341097,... |
from fastecdsa import curvemath
from .curve import P256
from .util import validate_type
class CurveMismatchError(Exception):
def __init__(self, curve1, curve2):
self.msg = 'Tried to add points on two different curves <{}> & <{}>'.format(
curve1.name, curve2.name)
class Point:
"""Represen... | {
"repo_name": "AntonKueltz/fastecdsa",
"path": "fastecdsa/point.py",
"copies": "1",
"size": "6019",
"license": "unlicense",
"hash": 3213975054331676000,
"line_mean": 31.5351351351,
"line_max": 98,
"alpha_frac": 0.5142050174,
"autogenerated": false,
"ratio": 3.717726991970352,
"config_test": fal... |
from fasteners import InterProcessLock
def open_locked(file, mode, buffering=-1, encoding=None, errors=None, newline=None, closefd=True):
"""
Returns an instance of `FileLock` that can be used in a with statement.
If used in a with statement, an inter-process lock for `file` will be acquired and the file... | {
"repo_name": "getsenic/senic-hub",
"path": "senic_hub/backend/lockfile.py",
"copies": "1",
"size": "1496",
"license": "mit",
"hash": 7984254286613660000,
"line_mean": 38.3684210526,
"line_max": 119,
"alpha_frac": 0.6677807487,
"autogenerated": false,
"ratio": 4.043243243243243,
"config_test": ... |
from fastforward.cliutil import priority
from playback.api import Glance
def make_target(user, hosts, key_filename, password):
try:
target = Glance(user, hosts, key_filename, password)
except AttributeError:
sys.stderr.write('No hosts found. Please using --hosts param.')
sys.exit(1)
... | {
"repo_name": "nofdev/fastforward",
"path": "fastforward/glance.py",
"copies": "1",
"size": "7138",
"license": "mit",
"hash": -8179799847800844000,
"line_mean": 50.7246376812,
"line_max": 142,
"alpha_frac": 0.4711403755,
"autogenerated": false,
"ratio": 5.195050946142649,
"config_test": false,
... |
from fastforward.cliutil import priority
from playback.api import Keystone
def make_target(args):
try:
target = Keystone(user=args.user, hosts=args.hosts.split(','), key_filename=args.key_filename, password=args.password)
except AttributeError:
err_hosts = 'No hosts found. Please using --hosts ... | {
"repo_name": "nofdev/fastforward",
"path": "fastforward/keystone.py",
"copies": "1",
"size": "8445",
"license": "mit",
"hash": 7942299746540057000,
"line_mean": 49.8734939759,
"line_max": 183,
"alpha_frac": 0.4706927176,
"autogenerated": false,
"ratio": 5.133738601823708,
"config_test": false,... |
from fast_gen import *
from learner import *
from pt_models import *
from dataset_pt import *
from sgdr_pt import *
from planet import *
bs=64; f_model = resnet34
path = "/data/jhoward/fast/planet/"
cv_idx = int(sys.argv[1])
torch.cuda.set_device(cv_idx % 4)
if cv_idx==1: torch.cuda.set_device(2)
n=len(list(open(f'{pa... | {
"repo_name": "jmhsi/justin_tinker",
"path": "data_science/courses/temp/courses/dl1/scripts/train_planet.py",
"copies": "1",
"size": "1126",
"license": "apache-2.0",
"hash": 828054421444281500,
"line_mean": 30.2777777778,
"line_max": 72,
"alpha_frac": 0.6190053286,
"autogenerated": false,
"ratio"... |
from fastkde import fastKDE
import pylab as PP
from numpy import *
from sklearn.preprocessing import scale
import numpy as np
from sklearn.pipeline import make_pipeline
from sklearn.linear_model import Ridge
from sklearn.preprocessing import PolynomialFeatures
import pandas as pd
from scipy.stats import pearsonr
f =... | {
"repo_name": "Diviyan-Kalainathan/causal-humans",
"path": "Cause-effect/Visualization_histo_estimation.py",
"copies": "1",
"size": "1818",
"license": "mit",
"hash": -9108658640046801000,
"line_mean": 19.4382022472,
"line_max": 68,
"alpha_frac": 0.6705170517,
"autogenerated": false,
"ratio": 2.53... |
from fastkml import kml
from shapely.geometry import Point, Polygon
import shapely.wkt
from datetime import datetime, timedelta, date
import urllib.request
import logging
import os.path
import tempfile
logging.basicConfig(level=logging.INFO)
logger = logging.getLogger("sentinel-downloader")
dir_path = os.path.dirname(... | {
"repo_name": "yoms/sentinel-banner-generator",
"path": "sentinel_downloader.py",
"copies": "1",
"size": "4654",
"license": "apache-2.0",
"hash": 1909509191873422600,
"line_mean": 36.232,
"line_max": 104,
"alpha_frac": 0.6317146541,
"autogenerated": false,
"ratio": 3.256822953114066,
"config_te... |
from fastlmm.association.FastLmmSet import *
#from fastlmm.util.distributable import *
from fastlmm.util.runner import *
import os
import sys
import time
import logging
if __name__ == "__main__":
logging.basicConfig(level=logging.INFO)
logging.info("last modified: %s" % time.ctime(os.path.getmtime(__... | {
"repo_name": "zhonghualiu/FaST-LMM",
"path": "fastlmm/association/FastLmmSetLOOC.py",
"copies": "1",
"size": "3266",
"license": "apache-2.0",
"hash": -308423665204733600,
"line_mean": 36.880952381,
"line_max": 97,
"alpha_frac": 0.5361298224,
"autogenerated": false,
"ratio": 3.8154205607476634,
... |
from fastlmm import Pr
import scipy as sp
import numpy as NP
from numpy import dot
import scipy.integrate
from scipy.linalg import cholesky,solve_triangular
from fastlmm.external.util.math import check_definite_positiveness,check_symmetry,mvnormpdf,ddot,trace2,dotd
from fastlmm.external.util.math import stl, stu
from f... | {
"repo_name": "MicrosoftGenomics/FaST-LMM",
"path": "fastlmm/inference/laplace.py",
"copies": "1",
"size": "16247",
"license": "apache-2.0",
"hash": -2821332079445614600,
"line_mean": 31.1722772277,
"line_max": 116,
"alpha_frac": 0.524219856,
"autogenerated": false,
"ratio": 3.1510861132660977,
... |
from fastlmm import Pr
import scipy as sp
import numpy as NP
from numpy import dot
import scipy.integrate
from scipy.linalg import cholesky,solve_triangular
from fastlmm.external.util.math import check_definite_positiveness,check_symmetry,mvnormpdf,ddot,trace2,dotd
from fastlmm.external.util.math import stl, stu... | {
"repo_name": "zhonghualiu/FaST-LMM",
"path": "fastlmm/inference/laplace.py",
"copies": "1",
"size": "16752",
"license": "apache-2.0",
"hash": -1115030270980705000,
"line_mean": 31.1722772277,
"line_max": 116,
"alpha_frac": 0.5084169054,
"autogenerated": false,
"ratio": 3.2389791183294663,
"con... |
from fastlmm.inference.fastlmm_predictor import FastLMM
from fastlmm.inference.linear_regression import LinearRegression
#from bin2kernel import Bin2Kernel
#from bin2kernel import makeBin2KernelAsEstimator
#from bin2kernel import Bin2KernelLaplaceLinearN
#from bin2kernel import getFastestBin2Kernel
#from bin2kernel i... | {
"repo_name": "MicrosoftGenomics/FaST-LMM",
"path": "fastlmm/inference/__init__.py",
"copies": "1",
"size": "1503",
"license": "apache-2.0",
"hash": 1471141754935287600,
"line_mean": 33.9534883721,
"line_max": 98,
"alpha_frac": 0.7325349301,
"autogenerated": false,
"ratio": 3.0736196319018405,
... |
from fastlmm.util.runner import *
import logging
import fastlmm.pyplink.plink as plink
import fastlmm.util.util as flutil
import numpy as np
from fastlmm.inference.lmm_cov import LMM as fastLMM
import scipy.stats as stats
from fastlmm.util.pickle_io import load, save
import time
import pandas as pd
def snp_set(
... | {
"repo_name": "zhonghualiu/FaST-LMM",
"path": "fastlmm/association/snp_set.py",
"copies": "1",
"size": "6642",
"license": "apache-2.0",
"hash": 2205989026791233500,
"line_mean": 31.4,
"line_max": 162,
"alpha_frac": 0.6242095754,
"autogenerated": false,
"ratio": 3.29300941993059,
"config_test": ... |
from fastlmm.util.runner import *
import logging
import unittest
import cStringIO
class DistributableTest(object) : #implements IDistributable
'''
This is a class for distributing any testing. It shouldn't be confused with TestDistributable which is a class for
testing the distributable classes.
'''
... | {
"repo_name": "MicrosoftGenomics/FaST-LMM",
"path": "fastlmm/util/distributabletest.py",
"copies": "1",
"size": "2803",
"license": "apache-2.0",
"hash": -2223977867477786600,
"line_mean": 33.6049382716,
"line_max": 120,
"alpha_frac": 0.5718872636,
"autogenerated": false,
"ratio": 4.01001430615164... |
from fastlmm.util.runner import *
import os
import base64
import numpy as SP
import sys
import logging
import zlib
class LocalMapper: # implements IRunner
def __init__(self, taskcount, output_file_ignored, mkl_num_threads,logging_handler=logging.StreamHandler(sys.stdout),instream=sys.stdin,outstream=sys... | {
"repo_name": "zhonghualiu/FaST-LMM",
"path": "fastlmm/util/runner/LocalMapper.py",
"copies": "1",
"size": "4989",
"license": "apache-2.0",
"hash": -973834173039295900,
"line_mean": 47.396039604,
"line_max": 234,
"alpha_frac": 0.5949087994,
"autogenerated": false,
"ratio": 3.8288564850345357,
"... |
from fastNetwork import FastNetwork
import featureExtraction
import wavFormatter
import phonemes
import random
import time
import os
def formatData(unformatted, formatted):
#get all of the files in the folder's subdirectories
folders = [x for x in os.listdir(unformatted)]
filePaths = [unformatted + "/" + f... | {
"repo_name": "WilliamWickerson/MLProject",
"path": "training.py",
"copies": "1",
"size": "3334",
"license": "mit",
"hash": 2211360858158372400,
"line_mean": 37.3333333333,
"line_max": 138,
"alpha_frac": 0.6841631674,
"autogenerated": false,
"ratio": 3.845444059976932,
"config_test": false,
"... |
from fastq import *
import sys
PRIMERS_FOR = {"c1a1_for": "CCCTGACCSAGACCTG",
"dqba1_for": "AGKCTTTGCGGATCCC",
"drba1_for": "CTGAGCTCCCSACTGG",
"c1a2_for": "CGACGGCAARGATTAC",
"c2a2_for": "GGAACAGCCAGAAGGA"
}
PRIMERS_REV = {"c1a1_re... | {
"repo_name": "imminfo/hex",
"path": "cut_primers.py",
"copies": "1",
"size": "2892",
"license": "apache-2.0",
"hash": -5842498047901458000,
"line_mean": 31.5056179775,
"line_max": 110,
"alpha_frac": 0.505186722,
"autogenerated": false,
"ratio": 2.889110889110889,
"config_test": false,
"has_n... |
from fast_rcnn.config import cfg, get_output_dir
import argparse
from utils.timer import Timer
import numpy as np
import cv2
from utils.cython_nms import nms, nms_new
from utils.boxes_grid import get_boxes_grid
import cPickle
import heapq
from utils.blob import im_list_to_blob
import os
import math
from rpn_msr.generat... | {
"repo_name": "Yuliang-Zou/Automatic_Group_Photography_Enhancement",
"path": "lib/fast_rcnn/test.py",
"copies": "1",
"size": "15292",
"license": "mit",
"hash": 3297628548486527000,
"line_mean": 37.039800995,
"line_max": 253,
"alpha_frac": 0.5789955532,
"autogenerated": false,
"ratio": 3.327965179... |
from fast_rcnn.config import cfg
from roi_data_layer.minibatch import get_minibatch
from roi_data_layer.roidb import prepare_roidb, add_bbox_regression_targets
import numpy as np
class RoIDataLayer:
def __init__(self, imdb, bbox_means, bbox_stds):
self.imdb = imdb
self._roidb = imdb.roidb
... | {
"repo_name": "danfeiX/scene-graph-TF-release",
"path": "lib/roi_data_layer/layer.py",
"copies": "1",
"size": "2548",
"license": "mit",
"hash": -5212336119103954000,
"line_mean": 37.6060606061,
"line_max": 75,
"alpha_frac": 0.5761381476,
"autogenerated": false,
"ratio": 3.4479025710419484,
"con... |
from fast_rcnn.config import cfg
from fast_rcnn.test import im_detect, im_feature
from fast_rcnn.nms_wrapper import nms
from utils.timer import Timer
from sklearn import svm
import matplotlib.pyplot as plt
import numpy as np
import scipy.io as sio
import caffe, os, sys, cv2
import argparse
CLASSES = ('__bac... | {
"repo_name": "bubae/gazeAssistRecognize",
"path": "functions/rcnnModule.py",
"copies": "1",
"size": "3544",
"license": "mit",
"hash": -5041780967071784000,
"line_mean": 28.3076923077,
"line_max": 114,
"alpha_frac": 0.6125846501,
"autogenerated": false,
"ratio": 2.621301775147929,
"config_test"... |
from fasttld import FastTLDExtract
import unittest
all_suffix = FastTLDExtract(exclude_private_suffix=False)
no_private_suffix = FastTLDExtract(exclude_private_suffix=True)
class FastTLDExtractCase(unittest.TestCase):
def test_all_suffix_trie(self):
trie = all_suffix.trie
self.assertEqual(trie['... | {
"repo_name": "jophy/fasttld",
"path": "tests/maintest.py",
"copies": "1",
"size": "4534",
"license": "mit",
"hash": -1222603523787870500,
"line_mean": 41.7735849057,
"line_max": 119,
"alpha_frac": 0.513233348,
"autogenerated": false,
"ratio": 3.302257829570284,
"config_test": true,
"has_no_k... |
from fate_arch.abc import AddressABC
class StandaloneAddress(AddressABC):
def __init__(self, home=None, name=None, namespace=None, storage_type=None):
self.home = home
self.name = name
self.namespace = namespace
self.storage_type = storage_type
class EggRollAddress(AddressABC):
... | {
"repo_name": "FederatedAI/FATE",
"path": "python/fate_arch/common/address.py",
"copies": "1",
"size": "1208",
"license": "apache-2.0",
"hash": -8261264498719091000,
"line_mean": 25.2608695652,
"line_max": 102,
"alpha_frac": 0.6150662252,
"autogenerated": false,
"ratio": 3.6717325227963524,
"co... |
from fate_arch.computing import ComputingEngine
from fate_arch.common.address import StandaloneAddress, EggRollAddress, HDFSAddress, MysqlAddress, FileAddress, \
PathAddress
class StorageEngine(object):
STANDALONE = 'STANDALONE'
EGGROLL = 'EGGROLL'
HDFS = 'HDFS'
MYSQL = 'MYSQL'
SIMPLE = 'SIMPL... | {
"repo_name": "FederatedAI/FATE",
"path": "python/fate_arch/storage/_types.py",
"copies": "1",
"size": "1992",
"license": "apache-2.0",
"hash": 2257306729606177500,
"line_mean": 22.7142857143,
"line_max": 113,
"alpha_frac": 0.6666666667,
"autogenerated": false,
"ratio": 3.074074074074074,
"conf... |
from faulhaber_const import commands as FMCC
fautmel=['GTYP', 'GSER', 'VER', 'GN', 'GCL', 'GRM', 'GKN', 'RM', 'KN', 'ANSW', 'NET', 'CST', 'CO', 'SO', 'TO', 'SAVE', 'BAUD', 'NODEADR', 'GNODEADR', 'GADV', 'EN', 'DI', 'GTIMEOUT', 'TIMEOUT', 'UPTIME', 'SADV']
from crcmod.predefined import mkCrcFun as mkCrcFunPre
from crcm... | {
"repo_name": "wittrup/crap",
"path": "python/fmcc.py",
"copies": "1",
"size": "1737",
"license": "mit",
"hash": 1262561970249405000,
"line_mean": 31.7735849057,
"line_max": 210,
"alpha_frac": 0.6183074266,
"autogenerated": false,
"ratio": 2.8016129032258066,
"config_test": false,
"has_no_key... |
from .faulkner.c3d import c3d as fc3d
from keras import backend as K
from keras.layers import Flatten, Dense, Input, Dropout
from keras.models import Model
import utilities.paths as paths
DRIVE = paths.get_drive()
def c3d(weights=None,
t=16,
fc_size=4096,
image_dims=112,
image_channel... | {
"repo_name": "HaydenFaulkner/phd",
"path": "keras_code/cnns/model_defs/c3d_wrapper.py",
"copies": "1",
"size": "4527",
"license": "mit",
"hash": -8803462639692740000,
"line_mean": 34.9365079365,
"line_max": 112,
"alpha_frac": 0.5045283852,
"autogenerated": false,
"ratio": 3.8396946564885495,
"... |
from fauxquests.exceptions import UnregisteredRequest
from fauxquests.messages import NOT_FOUND
from requests.compat import quote
from sdict import AlphaSortedDict
from six.moves.urllib.parse import parse_qs
import six
class Registry(dict):
"""A dict subclass that understands how to handle URLs with
keyword a... | {
"repo_name": "lukesneeringer/fauxquests",
"path": "fauxquests/utils.py",
"copies": "1",
"size": "5863",
"license": "bsd-3-clause",
"hash": -271715051634700580,
"line_mean": 35.64375,
"line_max": 76,
"alpha_frac": 0.5560293365,
"autogenerated": false,
"ratio": 4.295238095238095,
"config_test": ... |
from fbbot import element
class ButtonTemplate:
"""docstring for ButtonTemplate"""
def __init__(self, recipient_id, text, buttons=[]):
self.recipient_id = recipient_id
self.text = text
self.buttons = buttons
def add_button(self, button_type, title, metadata):
self.buttons.... | {
"repo_name": "espacioAntonio/fbbot",
"path": "fbbot/templates.py",
"copies": "1",
"size": "1724",
"license": "mit",
"hash": 6256698400450724000,
"line_mean": 32.8039215686,
"line_max": 79,
"alpha_frac": 0.6142691415,
"autogenerated": false,
"ratio": 4.124401913875598,
"config_test": false,
"... |
from fbchat import Client, log
from getpass import getpass
from datetime import datetime
from rest import Path_check, download_file, make_zip
from process import do_rest
import sys, os, urllib, time, socket, shutil, pyminizip, requests
socket.setdefaulttimeout(60)
reload(sys)
sys.setdefaultencoding("utf-8")
username ... | {
"repo_name": "satendrapandeymp/Facebook_message_download",
"path": "Message_linux.py",
"copies": "1",
"size": "1289",
"license": "mit",
"hash": -2124397079242172400,
"line_mean": 28.2954545455,
"line_max": 113,
"alpha_frac": 0.7051978278,
"autogenerated": false,
"ratio": 3.1060240963855423,
"c... |
from fbchat import Client, log
from getpass import getpass
from datetime import datetime
import sys, os, urllib, time, socket, shutil, requests
from glob import glob
from zipfile import ZipFile
socket.setdefaulttimeout(60)
reload(sys)
sys.setdefaultencoding("utf-8")
ending = '</div></div>'
username = str(raw_input("... | {
"repo_name": "satendrapandeymp/Facebook_message_download",
"path": "only_message.py",
"copies": "1",
"size": "5914",
"license": "mit",
"hash": -739968572152734600,
"line_mean": 26.3796296296,
"line_max": 335,
"alpha_frac": 0.6261413595,
"autogenerated": false,
"ratio": 2.8750607681088964,
"con... |
from fbchat import Client
from fbchat.models import *
from optparse import OptionParser
import getpass
import pickle as pkl
import principalScreen
from queue import Queue
import logging
from classUtil import *
_SESSION_FILE = "sessions.pkl"
parser = OptionParser(usage='Usage: %prog [options]')
parser.add_option("-u",... | {
"repo_name": "touir1/Facebook-Chat-Terminal-mode",
"path": "fbchat_terminal_mode.py",
"copies": "1",
"size": "4439",
"license": "mit",
"hash": -7689239619373663000,
"line_mean": 30.9352517986,
"line_max": 107,
"alpha_frac": 0.6028384771,
"autogenerated": false,
"ratio": 4.152478952291862,
"con... |
from fbchat import Client
import sys
import time
__author__ = "tjd5526"
__version__ = "0.3"
SendMessage = "초기화"
SendImage = "초기화"
SendEmoticon = "초기화"
SelectEmoticon = 1
Id = str(input("페이스북 아이디를 입력하세요>"))
Pw = str(input("페이스북 비밀번호를 입력하세요>"))
fc = Client(Id, Pw)
time.sleep(1)
FindUser = str(input("... | {
"repo_name": "tjd5526/facebook-fbchat",
"path": "facebook messenger only send.py",
"copies": "1",
"size": "8300",
"license": "mit",
"hash": -4456435346929159700,
"line_mean": 31.1139240506,
"line_max": 68,
"alpha_frac": 0.6189140964,
"autogenerated": false,
"ratio": 2.414896891351185,
"config_... |
from fbchat import GroupData, User
def test_group_from_graphql(session):
data = {
"name": "Group ABC",
"thread_key": {"thread_fbid": "11223344"},
"image": None,
"is_group_thread": True,
"all_participants": {
"nodes": [
{"messaging_actor": {"__typ... | {
"repo_name": "carpedm20/fbchat",
"path": "tests/threads/test_group.py",
"copies": "1",
"size": "1493",
"license": "bsd-3-clause",
"hash": 1961274549847348000,
"line_mean": 29.9791666667,
"line_max": 74,
"alpha_frac": 0.4707464694,
"autogenerated": false,
"ratio": 3.726817042606516,
"config_tes... |
from fbchat import (
QuickReplyText,
QuickReplyLocation,
QuickReplyPhoneNumber,
QuickReplyEmail,
)
from fbchat._models._quick_reply import graphql_to_quick_reply
def test_parse_minimal():
data = {
"content_type": "text",
"payload": None,
"external_payload": None,
"d... | {
"repo_name": "carpedm20/fbchat",
"path": "tests/models/test_quick_reply.py",
"copies": "1",
"size": "1387",
"license": "bsd-3-clause",
"hash": 8895580795552440000,
"line_mean": 27.306122449,
"line_max": 70,
"alpha_frac": 0.6020187455,
"autogenerated": false,
"ratio": 3.4761904761904763,
"confi... |
from fbchat import Poll, PollOption
def test_poll_option_from_graphql_unvoted():
data = {
"id": "123456789",
"text": "abc",
"total_count": 0,
"viewer_has_voted": "false",
"voters": [],
}
assert PollOption(
text="abc", vote=False, voters=[], votes_count=0, id... | {
"repo_name": "carpedm20/fbchat",
"path": "tests/models/test_poll.py",
"copies": "1",
"size": "2635",
"license": "bsd-3-clause",
"hash": 8879662230995438000,
"line_mean": 27.0319148936,
"line_max": 85,
"alpha_frac": 0.4371916509,
"autogenerated": false,
"ratio": 3.6546463245492373,
"config_test... |
from FBDb import *
from FBExecute import FBExecute
fbExecute = FBExecute()
fbExecute.update_images()
# dict = {}
# dict["score"] = 0
# dict["score1"] = 0
# dict["score2"] = 0
# dict["profile"] = "https://www.facebook.com/DrIftekharAnam/"
# dict["actual"] = "yes"
# dict["friends"] = []
# dict["name"] = "Dr. Iftekhar A... | {
"repo_name": "indervirbanipal/profiling",
"path": "testApp/FBUpdateImages.py",
"copies": "2",
"size": "1216",
"license": "mit",
"hash": 5944323628397227000,
"line_mean": 56.9047619048,
"line_max": 537,
"alpha_frac": 0.7376644737,
"autogenerated": false,
"ratio": 2.7699316628701594,
"config_tes... |
from fbdownload.downloader import FacebookDownloader
import re
class FacebookGroupLister(FacebookDownloader):
'''
Returns or lists the groups that the given access token can access and is
a member of.
'''
def __init__(self, access_token):
'''
Creates a new instance.
:param access_token: A Facebo... | {
"repo_name": "simonmikkelsen/facebook-downloader",
"path": "fbdownload/grouplister.py",
"copies": "1",
"size": "1085",
"license": "mit",
"hash": 5370759908152380000,
"line_mean": 29.1388888889,
"line_max": 84,
"alpha_frac": 0.6460829493,
"autogenerated": false,
"ratio": 3.7937062937062938,
"co... |
from fbdownload.downloader import FacebookDownloader
import urllib2
import os.path
import json
from fbdownload.htmlhelper import HtmlHelper
import dateutil.parser
import time
class FacebookHtmlExporter(FacebookDownloader):
'''
Takes an object hirachy that is created from a json file
and exports the data as a Fac... | {
"repo_name": "simonmikkelsen/facebook-downloader",
"path": "fbdownload/htmlexporter.py",
"copies": "1",
"size": "17662",
"license": "mit",
"hash": 2074076020654733300,
"line_mean": 35.1926229508,
"line_max": 228,
"alpha_frac": 0.6014607632,
"autogenerated": false,
"ratio": 3.6244613174635747,
... |
from fbdownload.downloader import FacebookDownloader
class FacebookGroupDownloader(FacebookDownloader):
'''
Downloads data from a Facebook group, including events.
Images are not downloaded.
In this package of classes, that is done by the
FacebookHtmlExporter, even though a stand alone download
function wo... | {
"repo_name": "simonmikkelsen/facebook-downloader",
"path": "fbdownload/groupdownloader.py",
"copies": "1",
"size": "2371",
"license": "mit",
"hash": 581755201948528100,
"line_mean": 31.9305555556,
"line_max": 105,
"alpha_frac": 0.6512020245,
"autogenerated": false,
"ratio": 3.945091514143095,
... |
from FBExecute import *
from FBDb import *
from bson import ObjectId
import urllib2
from operator import itemgetter
import os
import operator
db_host = "localhost"
db_port = 27017
db_client = FBDb.connect(db_host, db_port)
list_exceptions = []
list_exceptions.append("md")
list_exceptions.append("mohammad")
list_excep... | {
"repo_name": "indervirbanipal/relational-social-media-search-engine",
"path": "testApp/FBSeedScores.py",
"copies": "2",
"size": "5880",
"license": "mit",
"hash": -1853020991939154700,
"line_mean": 30.2765957447,
"line_max": 114,
"alpha_frac": 0.5003401361,
"autogenerated": false,
"ratio": 4.0663... |
from FBExecute import *
from FBDb import *
from bson import ObjectId
import urllib
from operator import itemgetter
import os
db_host = "localhost"
db_port = 27017
db_client = FBDb.connect(db_host, db_port)
profiles = 0
profiles_watson = 0
cursor = db_client.facebook_db.buet3.find()
for person in cursor:
for profi... | {
"repo_name": "indervirbanipal/profiling",
"path": "testApp/FBTest.py",
"copies": "2",
"size": "1845",
"license": "mit",
"hash": -6220814594574643000,
"line_mean": 37.4375,
"line_max": 537,
"alpha_frac": 0.6964769648,
"autogenerated": false,
"ratio": 2.8828125,
"config_test": false,
"has_no_k... |
from FB import FB
from SB import SB
from NB import NB,NBTraining
from DB import DB
from Burst import Burst
import numpy as np
import struct
from ctypes import *
from GSMC0 import GSMC0
class burstFileHead(Structure):
_pack_ = 1
_fields_ = [ ("name", c_char*2)
, ("length", c_int16)
, ("sn", c_int16... | {
"repo_name": "RP7/R7-OCM",
"path": "src/host/python/gsmlib/burstfile.py",
"copies": "2",
"size": "3863",
"license": "apache-2.0",
"hash": 424625117274159500,
"line_mean": 23.4556962025,
"line_max": 73,
"alpha_frac": 0.5943567176,
"autogenerated": false,
"ratio": 2.299404761904762,
"config_test... |
from FB import FB
from SB import SB
from NB import NB
from DB import DB
class TS0:
__field__ = ([FB,SB]+[NB]*8)*5+[DB]
class TS1:
__field__ = ([NB]*48+[DB]*3)*2
class TST:
__field__ = ([NB]*12 + [DB])*2
class Frame:
def __init__(self):
self.ts0_off = 0
self.ts1_off = 0
self.tst_off = 0
def config(self,... | {
"repo_name": "RP7/R7-OCM",
"path": "src/host/python/gsmlib/TS.py",
"copies": "2",
"size": "1508",
"license": "apache-2.0",
"hash": -5907304605831910000,
"line_mean": 17.6296296296,
"line_max": 53,
"alpha_frac": 0.5583554377,
"autogenerated": false,
"ratio": 2.330757341576507,
"config_test": fa... |
from fb_messages_parser import parse_to_deep_qa
from fb_messages_aggregator import aggregate_stats_for_target_usr
# LICENSE INFORMATION HEADER
__author__ = "Logan Martel"
__copyright__ = "Copyleft (c) 2018, Logan Martel"
__credits__ = ["Logan Martel"]
__license__ = "ApacheV2.0"
__version__ = "0.1.0"
__maintainer__ = ... | {
"repo_name": "martelogan/fb-friendbot-factory",
"path": "app/python/fb_messages_args_parsing.py",
"copies": "1",
"size": "4069",
"license": "apache-2.0",
"hash": 3190454693990387700,
"line_mean": 50.5063291139,
"line_max": 110,
"alpha_frac": 0.6220201524,
"autogenerated": false,
"ratio": 3.89750... |
from fb.models import Author, RemoteAuthor, RemoteServer
from fb.domain.models import DomainAuthor, DomainRemoteServer
from fb.api.api_utils import RequestContext
def get_admin_service(request):
"""
exposes the Author service for calls from the frontend
"""
return AdminService(RequestContext(request)... | {
"repo_name": "CMPUT404W17/FoundBook",
"path": "fb/services/admin_service.py",
"copies": "1",
"size": "2882",
"license": "mit",
"hash": 5494472700564168000,
"line_mean": 29.6595744681,
"line_max": 111,
"alpha_frac": 0.6023594726,
"autogenerated": false,
"ratio": 4.158730158730159,
"config_test"... |
from fb.models import Author, RemoteAuthor, RemoteServer
from fb.domain.models import DomainAuthor
from fb.api.api_utils import RequestContext
from fb.services.remote_server_service import RemoteServerService
from fb.services.post_service import PostService
import json
def get_author_service(request):
"""
exp... | {
"repo_name": "CMPUT404W17/FoundBook",
"path": "fb/services/author_service.py",
"copies": "1",
"size": "13788",
"license": "mit",
"hash": -282653067958718980,
"line_mean": 33.3840399002,
"line_max": 116,
"alpha_frac": 0.5675950102,
"autogenerated": false,
"ratio": 4.389684813753582,
"config_tes... |
from fbs._compat import with_metaclass
def build_named_set(primitives, names):
return {names[v].lower() for v in primitives}
class FBSType(object):
BOOL = 0
BYTE = 1
UBYTE = 2
SHORT = 3
USHORT = 4
INT = 5
UINT = 6
FLOAT = 7
LONG = 8
ULONG = 9
DOUBLE = 10
STRING = ... | {
"repo_name": "adsharma/flattools",
"path": "fbs/fbs.py",
"copies": "1",
"size": "2345",
"license": "mit",
"hash": 5789966153373499000,
"line_mean": 20.3181818182,
"line_max": 86,
"alpha_frac": 0.510021322,
"autogenerated": false,
"ratio": 3.3404558404558404,
"config_test": false,
"has_no_key... |
from fbuild.builders.c import guess_static, guess_shared
from fbuild.builders import find_program
from fbuild.record import Record
from fbuild.path import Path
import fbuild.db
from optparse import make_option
import sys
def pre_options(parser):
group = parser.add_option_group('config options')
group.add_opti... | {
"repo_name": "phase/o",
"path": "fbuildroot.py",
"copies": "1",
"size": "1580",
"license": "mit",
"hash": 9197262922039116000,
"line_mean": 35.7441860465,
"line_max": 82,
"alpha_frac": 0.6037974684,
"autogenerated": false,
"ratio": 3.7799043062200957,
"config_test": false,
"has_no_keywords":... |
from fbuild.builders.cxx import guess_static
from fbuild.config import cxx as cxx_test
from fbuild.record import Record
from fbuild.path import Path
from fbuild.db import caches
from optparse import make_option
class Expat(cxx_test.Test):
expat_h = cxx_test.header_test('expat.h')
def __init__(self, *args, **... | {
"repo_name": "kirbyfan64/cppexpat",
"path": "fbuildroot.py",
"copies": "1",
"size": "1259",
"license": "mit",
"hash": 2762828688856912000,
"line_mean": 31.2820512821,
"line_max": 79,
"alpha_frac": 0.6179507546,
"autogenerated": false,
"ratio": 3.46831955922865,
"config_test": false,
"has_no_... |
from fb.utils import Task, is_uuid
from fb.models import Post, Author, PERMISSIONS, CONTENT_TYPE, RemoteAuthor
from fb.domain.models import DomainPost, DomainAuthor
from fb.services.github_service import GithubService
from fb.services.remote_post_service import get_remote_post_services
from fb.api.api_utils import Requ... | {
"repo_name": "CMPUT404W17/FoundBook",
"path": "fb/services/post_service.py",
"copies": "1",
"size": "11033",
"license": "mit",
"hash": -5519226139247684000,
"line_mean": 34.5903225806,
"line_max": 182,
"alpha_frac": 0.59575818,
"autogenerated": false,
"ratio": 3.8944581715495943,
"config_test"... |
from fbx import *
import sys
def InitializeSdkObjects():
# The first thing to do is to create the FBX SDK manager which is the
# object allocator for almost all the classes in the SDK.
lSdkManager = FbxManager.Create()
if not lSdkManager:
sys.exit(0)
# Create an IOSettings object
... | {
"repo_name": "cloudteampro/juma-editor",
"path": "editor/lib/juma/AssetEditor/converters/FbxCommon.py",
"copies": "2",
"size": "3006",
"license": "mit",
"hash": -2444945835191503400,
"line_mean": 42.5652173913,
"line_max": 102,
"alpha_frac": 0.6999334664,
"autogenerated": false,
"ratio": 3.56161... |
from fbx import *
import sys
def InitializeSdkObjects():
# The first thing to do is to create the FBX SDK manager which is the
# object allocator for almost all the classes in the SDK.
lSdkManager = KFbxSdkManager.Create()
if not lSdkManager:
sys.exit(0)
# Create an IOSe... | {
"repo_name": "ARMistice/TestWelt",
"path": "utils/exporters/fbx/modules/win/Python26_x86/FbxCommon.py",
"copies": "21",
"size": "3045",
"license": "mit",
"hash": 4636418414371041000,
"line_mean": 43.4776119403,
"line_max": 111,
"alpha_frac": 0.6857142857,
"autogenerated": false,
"ratio": 3.56975... |
from fca.concept import Concept
from fca.ConceptSystem import ConceptSystem
def norris(context):
# To be more efficient we store intent (as Python set) of every
# object to the list
# TODO: Move to Context class?
examples = []
for ex in context.examples():
examples.append(ex)
cs = [Co... | {
"repo_name": "abramovd/Newster",
"path": "newster/fca/LatticeBuild.py",
"copies": "1",
"size": "1084",
"license": "mit",
"hash": 8003542465460550000,
"line_mean": 32.90625,
"line_max": 75,
"alpha_frac": 0.520295203,
"autogenerated": false,
"ratio": 4.1692307692307695,
"config_test": false,
"... |
from fca.concept import Concept
class Context:
def __init__(self, table, objects, attributes):
self.table = table
self.objects = objects
self.attributes = attributes
self.width = len(attributes)
self.height = len(objects)
def upperNeighbors(self, concept):
A = concept.extent
M = set(self._objectsIte... | {
"repo_name": "havrlant/fca-search",
"path": "src/fca/context.py",
"copies": "1",
"size": "2556",
"license": "bsd-2-clause",
"hash": 3297635621825130500,
"line_mean": 21.8303571429,
"line_max": 67,
"alpha_frac": 0.6529733959,
"autogenerated": false,
"ratio": 2.8979591836734695,
"config_test": f... |
#from fca.concept_lattice import ConceptLattice
import simplejson as json # no jython: import jsonm
from django.core.serializers import serialize
from django.db.models.query import QuerySet
#from io import StringIO
from django.db.models import Model
from django.utils.encoding import smart_unicode
import fca
impor... | {
"repo_name": "ksiomelo/cubix",
"path": "processing/utils.py",
"copies": "1",
"size": "18699",
"license": "apache-2.0",
"hash": -7648978675418574000,
"line_mean": 35.0289017341,
"line_max": 159,
"alpha_frac": 0.5373549388,
"autogenerated": false,
"ratio": 4.1387782204515275,
"config_test": fals... |
from fca.context import Context
from fuzzy.structures.lukasiewicz import Lukasiewicz
from fuzzy.FuzzySet import FuzzySet
from fuzzy.fca.fuzzy_concept import FuzzyConcept
class FuzzyContext(Context):
def __init__(self, table, objects, attributes):
super(FuzzyContext, self).__init__(table, objects, attributes)
self... | {
"repo_name": "havrlant/fca-search",
"path": "src/fuzzy/fca/fuzzy_context.py",
"copies": "1",
"size": "3283",
"license": "bsd-2-clause",
"hash": 8691922073052763000,
"line_mean": 22.6258992806,
"line_max": 79,
"alpha_frac": 0.6448370393,
"autogenerated": false,
"ratio": 2.805982905982906,
"conf... |
from fca_extension.utilities import getContextFromSR, context2slf,\
getFuzzyContext
from fca.concept import Concept
from common.io import trySaveFile
from other.constants import DATA_FOLDER
from fuzzy.fca.fuzzy_concept import FuzzyConcept
from fuzzy.FuzzySet import FuzzySet
from retrieval.spell_checker import SpellChe... | {
"repo_name": "havrlant/fca-search",
"path": "src/fca_extension/fca_search_engine.py",
"copies": "1",
"size": "9629",
"license": "bsd-2-clause",
"hash": -6547504728912050000,
"line_mean": 34.5350553506,
"line_max": 124,
"alpha_frac": 0.7378751688,
"autogenerated": false,
"ratio": 3.20752831445702... |
from fca.LatticeBuild import norris
from fca.ConceptSystem import ConceptSystem
from functools import cmp_to_key
class ConceptLattice(object):
"""ConceptLattice class
Examples
========
# >>> from fca import (Context, Concept)
# >>> ct = [[True, False, False, True],\
# [True, False, True... | {
"repo_name": "abramovd/Newster",
"path": "newster/fca/concept_lattice.py",
"copies": "1",
"size": "6349",
"license": "mit",
"hash": 5076539228243886000,
"line_mean": 29.8203883495,
"line_max": 91,
"alpha_frac": 0.4931485273,
"autogenerated": false,
"ratio": 3.6114903299203642,
"config_test": f... |
from fcc_utils2 import mipTableScan
from ceda_cc_config.config_c4 import CC_CONFIG_DIR
import re, os, string
ml = ['CORDEX_3h', 'CORDEX_6h', 'CORDEX_Aday', 'CORDEX_day', 'CORDEX_grids', 'CORDEX_mon' ]
ml = ['CORDEX_3h', 'CORDEX_6h', 'CORDEX_fx', 'CORDEX_day', 'CORDEX_mon', 'CORDEX_sem' ]
newMip = 'SPECS'
newMip = 'COR... | {
"repo_name": "martinjuckes/ceda_cc",
"path": "ceda_cc/comp_mip.py",
"copies": "1",
"size": "12564",
"license": "bsd-3-clause",
"hash": 5146502828486713000,
"line_mean": 38.8857142857,
"line_max": 206,
"alpha_frac": 0.5285737026,
"autogenerated": false,
"ratio": 2.6044776119402986,
"config_test... |
from fcfs import *
import heapq
# Format the queue for output
def format_queue(queue):
queue.sort(key=lambda x: x[1].get_process_id())
output = "[Q"
if len(queue) == 0:
output += " <empty>]"
else:
for i in range(len(queue)):
if i == (len(queue) - 1):
output += " " + str(queue[i][1]) + "]"
... | {
"repo_name": "faroos3/OpSysProject1",
"path": "srt.py",
"copies": "1",
"size": "9812",
"license": "mit",
"hash": -2682632519010425000,
"line_mean": 34.7565543071,
"line_max": 162,
"alpha_frac": 0.6366693844,
"autogenerated": false,
"ratio": 3.046258925799441,
"config_test": false,
"has_no_ke... |
from fcgiproto.constants import (
FCGI_BEGIN_REQUEST, FCGI_PARAMS, FCGI_STDIN, FCGI_STDOUT, FCGI_END_REQUEST, FCGI_DATA,
FCGI_FILTER, FCGI_AUTHORIZER, FCGI_ABORT_REQUEST, FCGI_REQUEST_COMPLETE)
from fcgiproto.events import (
RequestDataEvent, RequestSecondaryDataEvent, RequestAbortEvent, RequestBeginEvent)
... | {
"repo_name": "agronholm/fcgiproto",
"path": "fcgiproto/states.py",
"copies": "1",
"size": "4037",
"license": "mit",
"hash": 31489599784041864,
"line_mean": 43.8555555556,
"line_max": 99,
"alpha_frac": 0.5969779539,
"autogenerated": false,
"ratio": 4.407205240174672,
"config_test": false,
"ha... |
from .fchollet.vgg16 import VGG16 as fvgg16
from .faulkner.vgg16_sep_layers import vgg16_sep_layers
from .faulkner.vgg16_split_merge import vgg16_split_merge, vgg16_post_merge
from .jacobgil.model import get_model as jvgg16
import keras
from keras import backend as K
from keras.layers import Flatten, Dense, Input, Dro... | {
"repo_name": "HaydenFaulkner/phd",
"path": "keras_code/cnns/model_defs/vgg16_twostream_wrapper.py",
"copies": "1",
"size": "3315",
"license": "mit",
"hash": 648564956889067300,
"line_mean": 36.6818181818,
"line_max": 130,
"alpha_frac": 0.5906485671,
"autogenerated": false,
"ratio": 3.4,
"confi... |
from .fchollet.vgg16 import VGG16 as fvgg16
from keras.applications.vgg16 import VGG16
from .faulkner.vgg16_sep_layers import vgg16_sep_layers
from .faulkner.vgg16_split_merge import vgg16_split_merge
from .jacobgil.model import get_model as jvgg16
import keras
from keras import backend as K
from keras.layers import F... | {
"repo_name": "HaydenFaulkner/phd",
"path": "keras_code/cnns/model_defs/vgg16_wrapper.py",
"copies": "1",
"size": "3899",
"license": "mit",
"hash": 5579764011831795000,
"line_mean": 36.1428571429,
"line_max": 154,
"alpha_frac": 0.5765580918,
"autogenerated": false,
"ratio": 3.6992409867172675,
... |
from FCM.Class_FCM import FuzzyCMeans
import numpy
#import matplotlib.pyplot as plt
# import pylab as pl
#from sklearn import datasets
# from sklearn.decomposition import PCA
# from sklearn.metrics import confusion_matrix
from numpy.random.mtrand import np
from numpy import zeros
from FCM.Class_ValidityMea... | {
"repo_name": "fabiolapozyk/IncrementalFCM",
"path": "Python/Test1.py",
"copies": "2",
"size": "4278",
"license": "cc0-1.0",
"hash": 3878781590180573700,
"line_mean": 31.6850393701,
"line_max": 94,
"alpha_frac": 0.5607760636,
"autogenerated": false,
"ratio": 3.4142059058260177,
"config_test": f... |
from fcntl import fcntl, F_GETFL, F_SETFL
import logging
import os
import subprocess
import sys
import util
""" A simple wrapper that makes interacting with docker less ugly. """
logger = logging.getLogger(__name__)
class Container:
""" Represents a running container. """
def __init__(self, container, job_d... | {
"repo_name": "djpetti/stoplight",
"path": "daemon/docker.py",
"copies": "1",
"size": "3660",
"license": "mit",
"hash": -8191358454156335000,
"line_mean": 29.5,
"line_max": 80,
"alpha_frac": 0.6038251366,
"autogenerated": false,
"ratio": 3.8284518828451883,
"config_test": false,
"has_no_keywo... |
from fcntl import ioctl
from socket import AF_INET, SOCK_DGRAM, inet_ntoa, socket
from struct import pack
from pathlib import Path
from pprint import pprint
from requests import Session
from requests.adapters import DEFAULT_POOLSIZE
from yaml import load, BaseLoader
def api(endpoint):
return 'https://api.cloudfl... | {
"repo_name": "tkiapril/cloudflare-updater",
"path": "update_cloudflare.py",
"copies": "1",
"size": "3360",
"license": "mit",
"hash": 3190489822002661400,
"line_mean": 34,
"line_max": 101,
"alpha_frac": 0.4410714286,
"autogenerated": false,
"ratio": 4.876632801161103,
"config_test": false,
"h... |
from fcntl import ioctl
import time
class LPS331AP:
"""Measure temperature, air pressure, indirectly altitude."""
_I2C_ADDRESS = 0x5C
_WHO_AM_I = 0x0F
_RES_ADDR = 0x10
_CTRL_REG1 = 0x20
_CTRL_REG2 = 0x21
_I2C_SLAVE = 0x0703
_ONE_SHOT_CONVERSION_TIME = 0.042 # 41545 us
__t... | {
"repo_name": "GCerar/pysnesens",
"path": "snesens/sensors/lps331ap.py",
"copies": "1",
"size": "3394",
"license": "mit",
"hash": 4262637798322260000,
"line_mean": 23.2428571429,
"line_max": 71,
"alpha_frac": 0.5695344726,
"autogenerated": false,
"ratio": 3.260326609029779,
"config_test": false... |
from fcntl import LOCK_EX, LOCK_NB
import glob
import os
import subprocess
import time
from deimos.cmd import Run
import deimos.flock
from deimos.logger import log
from deimos.timestamp import iso
from deimos._struct import _Struct
class Cleanup(_Struct):
def __init__(self, root="/tmp/deimos", optimistic=False)... | {
"repo_name": "mesosphere/deimos",
"path": "deimos/cleanup.py",
"copies": "5",
"size": "2434",
"license": "apache-2.0",
"hash": -7920623102420064000,
"line_mean": 32.3424657534,
"line_max": 74,
"alpha_frac": 0.5090386196,
"autogenerated": false,
"ratio": 4.203799654576857,
"config_test": false,... |
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