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from ferrox.lib.base import *
from ferrox.lib.formgen import FormGenerator
from ferrox.model import form
import webhelpers
import logging
import formencode
import sqlalchemy
from sqlalchemy import sql
log = logging.getLogger(__name__)
class NotesController(BaseController):
def _enforce_ownership(self, note):
... | {
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"path": "ferrox/controllers/notes.py",
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"ha... |
from ferrox.lib.base import *
from ferrox.lib.formgen import FormGenerator
from ferrox.model import form
import formencode
import logging
import sqlalchemy
log = logging.getLogger(__name__)
class NewsController(BaseController):
def index(self):
"""Paged list of all news."""
page_link_var = 'p'
... | {
"repo_name": "NetShepsky/Ferrox",
"path": "ferrox/controllers/news.py",
"copies": "2",
"size": "4272",
"license": "mit",
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from ferrox.lib.base import *
from ferrox.model import form
import logging
log = logging.getLogger(__name__)
class StylesheetsController(BaseController):
duality_light_back = '#d4dce8'
duality_light_mid = '#919bad'
duality_light_fore = '#4b4b4b'
duality_dark_back = '#2e3b41'
duality_dark_mid = ... | {
"repo_name": "hsuaz/ferrox",
"path": "ferrox/controllers/stylesheets.py",
"copies": "2",
"size": "1842",
"license": "mit",
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from ferrox.lib.base import *
from pylons.decorators.secure import *
from ferrox.controllers.gallery import get_submission
from ferrox.controllers.journal import get_journal
class EditlogController(BaseController):
@check_perm('editlog')
def news(self, id=None):
"""Edit log for a news post."""
... | {
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"path": "ferrox/controllers/editlog.py",
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"license": "mit",
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from ferrox.lib.base import *
import sqlalchemy.exceptions
import os
import mimetypes
imagestore = os.getcwd() + '/ferrox/public/data'
imageurl = '/data'
class ImageManagerException(Exception):
pass
class ImageManagerExceptionFileExists(ImageManagerException):
pass
class ImageManagerExceptionFileNotFound(I... | {
"repo_name": "NetShepsky/Ferrox",
"path": "ferrox/lib/filestore.py",
"copies": "2",
"size": "4124",
"license": "mit",
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"line_max": 79,
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"autogenerated": false,
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"config_test": false,
"has_... |
from ferrox.lib.bbcode import *
import ferrox.lib.helpers as h
import ferrox.model
#from base import h
#from ferrox import model
class User(TagBase):
no_close = True
def __init__(self):
self.last_error = None
pass
def start(self, name, params):
if not params:
self.last... | {
"repo_name": "NetShepsky/Ferrox",
"path": "ferrox/lib/bbcode_for_fa.py",
"copies": "2",
"size": "2694",
"license": "mit",
"hash": 3472809722996039000,
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"line_max": 189,
"alpha_frac": 0.631403118,
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from ferrox.model.db import BaseTable, DateTime, Enum, IP, Session
from ferrox.model.db.users import *
from ferrox.model.db.discussions import *
from sqlalchemy.orm import object_mapper, relation
from sqlalchemy.ext.declarative import DeclarativeMeta
class EditLog(BaseTable):
__tablename__ = 'editlog'
... | {
"repo_name": "hsuaz/ferrox",
"path": "ferrox/model/db/messages.py",
"copies": "2",
"size": "11107",
"license": "mit",
"hash": 1143480556079416700,
"line_mean": 46.063559322,
"line_max": 125,
"alpha_frac": 0.6074547583,
"autogenerated": false,
"ratio": 3.9611269614835947,
"config_test": true,
... |
from ferrox.model.db import BaseTable
from ferrox.model.db.users import *
class Config(BaseTable):
"""
Table to contain all config parametrs
"""
__tablename__ = 'config'
id = Column(types.Integer, primary_key=True)
section = Column(types.String(length=32), nullable=False)
name = Column... | {
"repo_name": "hsuaz/ferrox",
"path": "ferrox/model/db/config.py",
"copies": "2",
"size": "3283",
"license": "mit",
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"line_mean": 27.0598290598,
"line_max": 121,
"alpha_frac": 0.548888212,
"autogenerated": false,
"ratio": 4.285900783289817,
"config_test": false,
"... |
from festively.models import FestivalEntry
from urllib2 import urlopen
import json
def convert_keys_to_string(dictionary):
"""Recursively converts dictionary keys to strings."""
if not isinstance(dictionary, dict):
return dictionary
return dict((str(k), convert_keys_to_string(v))
f... | {
"repo_name": "avidas/festive.ly",
"path": "mongo_batch_load.py",
"copies": "1",
"size": "1263",
"license": "mit",
"hash": -6835127349686031000,
"line_mean": 37.2727272727,
"line_max": 109,
"alpha_frac": 0.622327791,
"autogenerated": false,
"ratio": 3.7477744807121662,
"config_test": false,
"... |
from .fetcher import fetch_hash_tweets, fetch_user_tweets
from .decorators import filter_tweets, extract_new_criterion
from celery import task
from indexer.models import Tweet
from django.db import transaction
import re
from datetime import datetime
import time
from mongoengine.queryset import NotUniqueError
month_to_... | {
"repo_name": "myaser/DAPOS_corpus_browser",
"path": "corpus_browser/scrapper/crawler/__init__.py",
"copies": "1",
"size": "2818",
"license": "mit",
"hash": -2163843396213570600,
"line_mean": 33.3658536585,
"line_max": 151,
"alpha_frac": 0.6156848829,
"autogenerated": false,
"ratio": 3.3951807228... |
from fetcher import fetch
from robotsparser import RobotsParser
from linkcollector import LinkCollector
from workqueue import WorkQueue
from dbhandler import dbhandler
from urlobj import URLObj
from blacklist import Blacklist
from urllist import URLList
import traceback
import logging
import os.path
import sys
class ... | {
"repo_name": "get9/monkeyshines",
"path": "crawler.py",
"copies": "1",
"size": "3134",
"license": "apache-2.0",
"hash": -285795350685175780,
"line_mean": 34.6136363636,
"line_max": 96,
"alpha_frac": 0.5421186981,
"autogenerated": false,
"ratio": 4.4390934844192635,
"config_test": false,
"has... |
from ..fetcher_utils import *
from ..extract import extract
def parse_xml(x):
'''
parse xml
:param path: [String] Path to an xml file
:return: object of class lxml.etree._Element
Usage::
from pyminer import fetch
from pyminer import parsers
url = "https://peerj.com/articl... | {
"repo_name": "sckott/pyminer",
"path": "pyminer/parsers/parsers.py",
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"size": "1696",
"license": "mit",
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"line_mean": 21.6133333333,
"line_max": 86,
"alpha_frac": 0.59375,
"autogenerated": false,
"ratio": 3.533333333333333,
"config_test": false,
"has... |
from fetch import session, Base, engine, Users, Record, UserInfo, ItemInfo
from sqlalchemy.sql.expression import func
from scipy.sparse import csr_matrix
import cPickle
fr = open('dat/mat.dat','rb')
cPickle.load(fr)
tableUI = cPickle.load(fr)
tableII = cPickle.load(fr)
fr.close()
imask=dict()
umask=dict()
# all item... | {
"repo_name": "wattlebird/Chi",
"path": "sim/construct_mask.py",
"copies": "1",
"size": "2269",
"license": "mit",
"hash": -1501639379886698500,
"line_mean": 30.9718309859,
"line_max": 95,
"alpha_frac": 0.6602027325,
"autogenerated": false,
"ratio": 2.7569866342648846,
"config_test": false,
"h... |
from .fetchlib import (getTopArticles, getBestArticles,
getNewArticles, getSavedArticles)
from .utils import (printArticles, openItem,
clearSavedArticles, removeSavedArticles)
from .parsing.cli import getParser
def main(args=[]):
hfeedParser = getParser()
p = hfeedP... | {
"repo_name": "pratyushprakash/HackerFeed",
"path": "hfeedlib/hfeed_main.py",
"copies": "1",
"size": "1641",
"license": "mit",
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"line_mean": 32.4897959184,
"line_max": 79,
"alpha_frac": 0.5947592931,
"autogenerated": false,
"ratio": 3.447478991596639,
"config_test":... |
from fetch_linkedin import return_tidy_ln_data
from fetch_gh import return_tidy_gh_data
from tidy_tex import *
from person_info import person_info
#Fetch profile data
names,education,skills,interests,awards,positions = return_tidy_ln_data()
gh_user,repo_list = return_tidy_gh_data()
#Handle data
#Write data to te... | {
"repo_name": "osheadavid7/py-resume",
"path": "py-resume.py",
"copies": "1",
"size": "1053",
"license": "mit",
"hash": -4653269919582747000,
"line_mean": 20.9375,
"line_max": 93,
"alpha_frac": 0.6828110161,
"autogenerated": false,
"ratio": 2.7,
"config_test": false,
"has_no_keywords": true,
... |
from fever.scorer import evidence_macro_precision
import unittest
class PrecisionTestCase(unittest.TestCase):
def test_precision_nei_no_contribution_to_score(self):
instance = {"label": "not enough info", "predicted_label": "not enough info"}
p,h = evidence_macro_precision(instance)
self.... | {
"repo_name": "sheffieldnlp/fever-scorer",
"path": "tests/test_precision.py",
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"size": "2555",
"license": "apache-2.0",
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"line_max": 187,
"alpha_frac": 0.6512720157,
"autogenerated": false,
"ratio": 3.4527027027027026,
"conf... |
from fever.scorer import evidence_macro_recall, evidence_macro_precision, fever_score
import unittest
class MaxEvidenceTestCase(unittest.TestCase):
def test_recall_partial_predictions_same_groups_zero_score(self):
instance = {"label": "supports", "predicted_label": "supports","evidence":[[[None,None,"pa... | {
"repo_name": "sheffieldnlp/fever-scorer",
"path": "tests/test_max_evidence.py",
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"size": "3790",
"license": "apache-2.0",
"hash": 1964485275384592600,
"line_mean": 48.2207792208,
"line_max": 174,
"alpha_frac": 0.6237467018,
"autogenerated": false,
"ratio": 3.1374172185430464,
"co... |
from fever.scorer import evidence_macro_recall
import unittest
class RecallTestCase(unittest.TestCase):
def test_recall_nei_no_contribution_to_score(self):
instance = {"label": "not enough info", "predicted_label": "not enough info"}
p,h = evidence_macro_recall(instance)
self.assertEqual(... | {
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"alpha_frac": 0.628,
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"ratio": 3.3809166040571,
"config_test": tru... |
from fextract import FeatureExtractor
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
class GuitarTranscriber(object):
"""
Trains and tests ML model for guitar transcription
"""
def __init__(self, audio_path, label_path, window_size, hop_size, sampling_rate):
... | {
"repo_name": "Guitar-Machine-Learning-Group/guitar-transcriber",
"path": "guitar_transcriber.py",
"copies": "1",
"size": "1830",
"license": "mit",
"hash": 3111343578160467000,
"line_mean": 34.8823529412,
"line_max": 91,
"alpha_frac": 0.631147541,
"autogenerated": false,
"ratio": 3.85263157894736... |
from ffflash.inc.nodelist import handle_nodelist
from ffflash.inc.sidecars import handle_sidecars
from ffflash.info import info
from ffflash.lib.api import FFApi
from ffflash.lib.args import parsed_args
from ffflash.lib.clock import get_iso_timestamp
from ffflash.lib.files import check_file_location, dump_file, load_fi... | {
"repo_name": "spookey/ffflash",
"path": "ffflash/main.py",
"copies": "1",
"size": "3890",
"license": "bsd-3-clause",
"hash": -601157330116850000,
"line_mean": 29.6299212598,
"line_max": 79,
"alpha_frac": 0.5604113111,
"autogenerated": false,
"ratio": 3.7877312560856864,
"config_test": false,
... |
from ffflash.inc.rankfile import handle_rankfile
from ffflash.lib.files import check_file_location, load_file
from ffflash.lib.remote import fetch_www_struct
from ffflash.lib.text import replace_text
def _nodelist_fetch(ff):
'''
Determines if ``--nodelist`` was a file or a url, and tries to fetch it.
Vali... | {
"repo_name": "spookey/ffflash",
"path": "ffflash/inc/nodelist.py",
"copies": "1",
"size": "4226",
"license": "bsd-3-clause",
"hash": 9002069659632684000,
"line_mean": 31.2595419847,
"line_max": 78,
"alpha_frac": 0.6157122575,
"autogenerated": false,
"ratio": 3.820976491862568,
"config_test": f... |
from ffflash.inc.sidecars import _sidecar_path
def test_sidecar_path_is_folder(tmpdir, fffake, capsys):
sc = tmpdir.ensure('sidecar', dir=True)
ff = fffake(tmpdir.join('api_file.json', dry=True))
assert tmpdir.listdir() == [sc]
assert _sidecar_path(ff, str(sc)) == (False, None, None)
out, err = ... | {
"repo_name": "spookey/ffflash",
"path": "tests/inc/sidecars/test_sidecar_path.py",
"copies": "1",
"size": "1751",
"license": "bsd-3-clause",
"hash": -3764349161337197000,
"line_mean": 29.1896551724,
"line_max": 75,
"alpha_frac": 0.6287835523,
"autogenerated": false,
"ratio": 2.9379194630872485,
... |
from ffflash.lib.files import dump_file, load_file
from ffflash.lib.locations import check_file_extension, check_file_location
from ffflash.lib.struct import merge_dicts
def _sidecar_path(ff, sc):
'''
Check passed sidecars for valid paths, format (*json* or *yaml*) and for
valid filenames (no double dots)... | {
"repo_name": "spookey/ffflash",
"path": "ffflash/inc/sidecars.py",
"copies": "1",
"size": "4335",
"license": "bsd-3-clause",
"hash": -4281945453374759000,
"line_mean": 30.6423357664,
"line_max": 79,
"alpha_frac": 0.6053056517,
"autogenerated": false,
"ratio": 3.6737288135593222,
"config_test":... |
from ffflash.lib.locations import check_file_extension
def test_check_file_extension_no_ext():
assert check_file_extension('file') == (None, None)
assert check_file_extension('file', '') == (None, None)
assert check_file_extension('file.txt') == (None, None)
assert check_file_extension('file.txt', ''... | {
"repo_name": "spookey/ffflash",
"path": "tests/lib/locations/test_check_file_extension.py",
"copies": "1",
"size": "1451",
"license": "bsd-3-clause",
"hash": -1539398907849753600,
"line_mean": 32.7441860465,
"line_max": 76,
"alpha_frac": 0.5892487939,
"autogenerated": false,
"ratio": 3.268018018... |
from ffflash.lib.locations import check_file_location
def test_check_file_location_empty():
assert check_file_location('', must_exist=True) is None
assert check_file_location('', must_exist=False) is None
def test_check_file_location_on_folders(tmpdir):
pf = tmpdir.join('parent')
ne = pf.join('does_... | {
"repo_name": "spookey/ffflash",
"path": "tests/lib/locations/test_check_file_location.py",
"copies": "1",
"size": "1095",
"license": "bsd-3-clause",
"hash": -7182357606300125000,
"line_mean": 31.2058823529,
"line_max": 68,
"alpha_frac": 0.6913242009,
"autogenerated": false,
"ratio": 3.2981927710... |
from ffflash.lib.struct import merge_dicts
def test_merge_dicts_on_faulty_input():
assert merge_dicts(None, None) is None
assert merge_dicts({}, None) is None
assert merge_dicts(None, {}) is None
assert merge_dicts('a', {}) == 'a'
assert merge_dicts({}, 'b') == 'b'
assert merge_dicts(False, {... | {
"repo_name": "spookey/ffflash",
"path": "tests/lib/struct/test_merge_dicts.py",
"copies": "1",
"size": "1576",
"license": "bsd-3-clause",
"hash": -109935409336821170,
"line_mean": 24.8360655738,
"line_max": 71,
"alpha_frac": 0.3769035533,
"autogenerated": false,
"ratio": 2.6986301369863015,
"c... |
from ffflash.lib.text import make_pretty
class FFApi:
'''
Helper class provide some easy way to access and modify dictionaries.
It only provides reading and replacing already existing keys.
:param content: The initial data to work with
'''
def __init__(self, content):
self.c = content... | {
"repo_name": "spookey/ffflash",
"path": "ffflash/lib/api.py",
"copies": "1",
"size": "1340",
"license": "bsd-3-clause",
"hash": -3891313097111071000,
"line_mean": 27.5106382979,
"line_max": 73,
"alpha_frac": 0.5343283582,
"autogenerated": false,
"ratio": 4.407894736842105,
"config_test": false... |
from ffflash.lib.text import replace_text
def test_replace_text_empy_input():
for rx, rpl, txt in [
('', '', ''), (r'', '', ''),
('', '', 'unchanged'), (r'', '', 'unchanged'),
('', '', None), (r'', '', None),
('', None, None), (r'', None, None),
(None, None, None)
]:
... | {
"repo_name": "spookey/ffflash",
"path": "tests/lib/text/test_replace_text.py",
"copies": "1",
"size": "1024",
"license": "bsd-3-clause",
"hash": 1920490701776240400,
"line_mean": 32.0322580645,
"line_max": 59,
"alpha_frac": 0.470703125,
"autogenerated": false,
"ratio": 2.790190735694823,
"conf... |
from ffflash.lib.text import search_text
def test_search_text_empty_input():
for rx, txt in [
(None, None), (r'', None), (None, ''), (r'', ''),
(r'a', None), (None, 'a'), (r'a', ''), (r'', 'a')
]:
assert not search_text(rx, txt)
def test_search_text():
assert not search_text(r'a'... | {
"repo_name": "spookey/ffflash",
"path": "tests/lib/text/test_search_text.py",
"copies": "1",
"size": "1067",
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"hash": 2616897481657661000,
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"line_max": 58,
"alpha_frac": 0.5276476101,
"autogenerated": false,
"ratio": 2.6151960784313726,
"con... |
from ffig.clang.cindex import CursorKind, TypeKind
def _set_impl_name(o):
o.impl_name = o.name
names = [a for a in o.annotations if a.startswith("FFIG:NAME:")]
if names:
o.name = names[-1].replace("FFIG:NAME:", "")
def apply_class_annotations(model_class):
_set_impl_name(model_class)
fo... | {
"repo_name": "jbcoe/C_API_generation",
"path": "ffig/annotations.py",
"copies": "2",
"size": "1305",
"license": "mit",
"hash": -1669967945000203000,
"line_mean": 33.3421052632,
"line_max": 102,
"alpha_frac": 0.6030651341,
"autogenerated": false,
"ratio": 3.425196850393701,
"config_test": false... |
from ffig.cppmodel import TypeKind
import platform
# CPP filter to cast type if required
def restore_cpp_type(a):
t = a.type
n = a.name
if t.kind == TypeKind.VOID:
return n
if t.kind == TypeKind.INT:
return n
if t.kind == TypeKind.DOUBLE:
return n
if t.kind == TypeKind... | {
"repo_name": "FFIG/ffig",
"path": "ffig/filters/capi_filter.py",
"copies": "2",
"size": "20593",
"license": "mit",
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"line_mean": 31.7392686804,
"line_max": 110,
"alpha_frac": 0.5858301365,
"autogenerated": false,
"ratio": 3.4856127285037237,
"config_test": false,
... |
from .ffi import ffi, lib
from .ffi.lib import *
try:
from threading import local as _thread_local
except ImportError:
class _thread_local: pass # there's only one thread anyway
_thread_local = _thread_local()
def _str(b):
'''struct cno_buffer_t -> str'''
return str(ffi.buffer(b.data, b.size), 'utf-... | {
"repo_name": "pyos/libcno",
"path": "python/cno/raw.py",
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"size": "5339",
"license": "mit",
"hash": -5221739984270146000,
"line_mean": 34.8322147651,
"line_max": 141,
"alpha_frac": 0.5740775426,
"autogenerated": false,
"ratio": 3.120397428404442,
"config_test": false,
"has_no_k... |
from ffindex.content import FFIndexContent
import mmap
try:
isinstance("", basestring)
def _is_string(s):
return isinstance(s, basestring)
except NameError:
def _is_string(s):
return isinstance(s, str)
def _to_file(fn, mode="rb"):
if _is_string(fn):
return open(fn, mode)
... | {
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"path": "ffindex/__init__.py",
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"autogenerated": false,
"ratio": 3.2280701754385963,
"config_test": false,
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from fflop import filter
import math
import numpy as np
import statsmodels as sm2
def ar_forecast(data, smoothing=False):
if smoothing:
data = double_exponential_smoother(data)[1]
try:
model = sm2.tsa.ar_model.AR(data).fit()
value = model.predict(len(data), len(data) + 20)
r... | {
"repo_name": "jacksonicson/paper.IS2015",
"path": "control/Control/src/workload/forecasting.py",
"copies": "1",
"size": "3303",
"license": "mit",
"hash": 1867838515739491800,
"line_mean": 24.0227272727,
"line_max": 95,
"alpha_frac": 0.5504087193,
"autogenerated": false,
"ratio": 3.14272121788772... |
from ffpicker.config import PickConfig
from ffpicker.data import models
import logging
import random
__all__ = [
"between",
"team_bias"
]
class PickContext(object):
def __init__(self, team1, team2):
self._team1 = models.team_from_str(team1)
self._team2 = models.team_from_str(team2)
@... | {
"repo_name": "YuHChen/fantasy-football-picker",
"path": "ffpicker/pick.py",
"copies": "1",
"size": "3666",
"license": "mit",
"hash": -7164554606882565000,
"line_mean": 29.2975206612,
"line_max": 91,
"alpha_frac": 0.614020731,
"autogenerated": false,
"ratio": 3.6550348953140577,
"config_test": ... |
from ffpicker.config import ScheduleXMLConfig
from ffpicker.data.models import Schedule
from ffpicker.data.models import Season as stypes
import logging
import os
import xml.etree.ElementTree as ET
__all__ = [
"schedule_xml_to_json"
]
_config = ScheduleXMLConfig()
def _game_xml_to_json(game_xml, stype=stypes.RE... | {
"repo_name": "YuHChen/fantasy-football-picker",
"path": "ffpicker/data/transform.py",
"copies": "1",
"size": "1781",
"license": "mit",
"hash": -1725301393581623600,
"line_mean": 36.1041666667,
"line_max": 86,
"alpha_frac": 0.636159461,
"autogenerated": false,
"ratio": 3.398854961832061,
"confi... |
from fframework import asfunction
from moviemaker3.stacks.stack import Stack
__all__ = ['AlphaStack']
class AlphaStack(Stack):
r"""The formula used for combination of layer `i` using layer `i + 1` is:
.. math::
X = X_i (1 - \alpha_{i + 1}) + X_{i + 1} \alpha_{i + 1}
where `X` is a Layer... | {
"repo_name": "friedrichromstedt/moviemaker3",
"path": "moviemaker3/stacks/alpha.py",
"copies": "1",
"size": "1088",
"license": "mit",
"hash": 8469979626869311000,
"line_mean": 31.9696969697,
"line_max": 77,
"alpha_frac": 0.5909926471,
"autogenerated": false,
"ratio": 4.029629629629629,
"config... |
from fframework import asfunction
from moviemaker3.stacks.stack import Stack
class WeightedStack(Stack):
"""Elements in the WeightedStack should return (*weight*, *layer*);
*layer* and *weight* are extracted by indexing (tuple assignment). You
might use ``fframework.compound()`` to generate tuple Functi... | {
"repo_name": "friedrichromstedt/moviemaker3",
"path": "moviemaker3/stacks/weighted.py",
"copies": "1",
"size": "1580",
"license": "mit",
"hash": 4292150807781797000,
"line_mean": 39.5128205128,
"line_max": 78,
"alpha_frac": 0.6158227848,
"autogenerated": false,
"ratio": 4.103896103896104,
"con... |
from fframework import OpFunction, asfunction
__all__ = ['Stack']
class Stack(OpFunction):
"""Base class for stacks with layers. Stacks can be used as layers."""
def __init__(self):
"""Initialises the ``.elements`` attribute to the empty list."""
self.elements = []
def __xor__(... | {
"repo_name": "friedrichromstedt/moviemaker3",
"path": "moviemaker3/stacks/stack.py",
"copies": "1",
"size": "1696",
"license": "mit",
"hash": -5624792536493413000,
"line_mean": 29.2857142857,
"line_max": 76,
"alpha_frac": 0.5919811321,
"autogenerated": false,
"ratio": 4,
"config_test": false,
... |
from fframework import OpFunction
__all__ = ['p']
class Ps(dict):
"""Holds a number of parameter values.
The values are hierarchical, this means that the name specifies a
slash-separated path. Setting a name containing a slash will set the
basename in some leaf ``Ps``. E.g.::
p... | {
"repo_name": "friedrichromstedt/moviemaker3",
"path": "moviemaker3/parameter.py",
"copies": "1",
"size": "3800",
"license": "mit",
"hash": 1224896842386575600,
"line_mean": 30.1475409836,
"line_max": 79,
"alpha_frac": 0.5428947368,
"autogenerated": false,
"ratio": 4.125950054288817,
"config_te... |
from .fft_tools import correlate2d, fast_ffts
from .fft_tools import dftups, upsample_image, shift
import warnings
import numpy as np
__all__ = ['register_images']
def register_images(im1, im2, usfac=1, return_registered=False,
return_error=False, zeromean=True, DEBUG=False, maxoff=None,
... | {
"repo_name": "keflavich/image_registration",
"path": "image_registration/register_images.py",
"copies": "1",
"size": "14072",
"license": "mit",
"hash": -8551048953051986000,
"line_mean": 42.0336391437,
"line_max": 171,
"alpha_frac": 0.5933058556,
"autogenerated": false,
"ratio": 3.28785046728971... |
from .fft_tools import zoom
import numpy as np
import matplotlib.pyplot as pl
def iterative_zoom(image, mindiff=1., zoomshape=[10,10],
return_zoomed=False, zoomstep=2, verbose=False,
minmax=np.min, ploteach=False, return_center=True):
"""
Iteratively zoom in on the *minimum* position in an imag... | {
"repo_name": "keflavich/image_registration",
"path": "image_registration/iterative_zoom.py",
"copies": "1",
"size": "8555",
"license": "mit",
"hash": -953963214077147500,
"line_mean": 38.976635514,
"line_max": 109,
"alpha_frac": 0.6031560491,
"autogenerated": false,
"ratio": 3.59151973131822,
... |
from fget.resource.artifact import Artifact
from fget.resource.base import Resource
class Build(Resource):
def __init__(self, url, number=None):
super(Build, self).__init__(url)
self.number = number
def get_caused_by(self):
response = self._request()
causes = None
for... | {
"repo_name": "atykhonov/fget",
"path": "fget/resource/build.py",
"copies": "1",
"size": "1064",
"license": "mit",
"hash": -1603310851268815600,
"line_mean": 24.9512195122,
"line_max": 77,
"alpha_frac": 0.5639097744,
"autogenerated": false,
"ratio": 4.15625,
"config_test": false,
"has_no_keyw... |
from fget.resource.base import Resource
from fget.resource.build import Build
from fget.resource.build import LastSuccessfulBuild
class Job(Resource):
builds = []
def __init__(self, url, job_name=None):
super(Job, self).__init__(url)
self.name = job_name
def get_name(self):
retu... | {
"repo_name": "atykhonov/fget",
"path": "fget/resource/job.py",
"copies": "1",
"size": "1182",
"license": "mit",
"hash": -6805216687867235000,
"line_mean": 30.1052631579,
"line_max": 74,
"alpha_frac": 0.5778341794,
"autogenerated": false,
"ratio": 4.1328671328671325,
"config_test": false,
"ha... |
from _fg.graph import Graph
from _ai._fg.node import FNode, VNode
def generate_graph():
v_a = VNode('A')
v_c = VNode('C')
v_j = VNode('J')
v_m = VNode('M')
v_t = VNode('T')
f_c = FNode('~C', [0.999, 0.001], v_c)
f_t = FNode('~T', [0.998, 0.002], v_t)
f_a_j = FNode('~AJ', [[0.99, 0.3]... | {
"repo_name": "ycaxgjd/VIL",
"path": "PGM/factor_graph/main.py",
"copies": "1",
"size": "1691",
"license": "mit",
"hash": 4876177740386978000,
"line_mean": 25.8412698413,
"line_max": 106,
"alpha_frac": 0.5044352454,
"autogenerated": false,
"ratio": 2.181935483870968,
"config_test": false,
"ha... |
from .fgir import *
from .error import *
import copy
# Optimization pass interfaces
class Optimization(object):
def visit(self, obj): pass
class FlowgraphOptimization(Optimization):
'''Called on each flowgraph in a FGIR.
May modify the flowgraph by adding or removing nodes (return a new Flowgraph).
If you mo... | {
"repo_name": "cs207-project/TimeSeries",
"path": "pype/optimize.py",
"copies": "2",
"size": "6249",
"license": "mit",
"hash": -8509276869426938000,
"line_mean": 36.1964285714,
"line_max": 110,
"alpha_frac": 0.687149944,
"autogenerated": false,
"ratio": 3.8934579439252337,
"config_test": false,... |
from .fgir import *
from .error import *
# Optimization pass interfaces
class Optimization(object):
def visit(self, obj): pass
class FlowgraphOptimization(Optimization):
'''Called on each flowgraph in a FGIR.
May modify the flowgraph by adding or removing nodes (return a new Flowgraph).
If you modify nodes,... | {
"repo_name": "mc-hammertimeseries/cs207project",
"path": "pype/optimize.py",
"copies": "1",
"size": "5100",
"license": "mit",
"hash": 1825987872668094500,
"line_mean": 37.9312977099,
"line_max": 108,
"alpha_frac": 0.7254901961,
"autogenerated": false,
"ratio": 3.512396694214876,
"config_test":... |
from .fgir import *
from .error import *
# Optimization pass interfaces
class Optimization(object):
def visit(self, obj): pass
class FlowgraphOptimization(Optimization):
'''Called on each flowgraph in a FGIR.
May modify the flowgraph by adding or removing nodes (return a new Flowgraph).
If you modify nodes, ... | {
"repo_name": "Planet-Nine/cs207project",
"path": "pype/optimize.py",
"copies": "1",
"size": "5807",
"license": "mit",
"hash": -3390475090232732700,
"line_mean": 38.2364864865,
"line_max": 110,
"alpha_frac": 0.6919235406,
"autogenerated": false,
"ratio": 3.8128693368351936,
"config_test": false... |
from .fgir import *
from .optimize import FlowgraphOptimization
from .error import Warn
from time import sleep
import asyncio
class PCodeOp(object):
'''A class interface for creating coroutines.
This helps us keep track of valid computational elements. Every coroutine in
a PCode object should be an method of PCod... | {
"repo_name": "mc-hammertimeseries/cs207project",
"path": "pype/pcode.py",
"copies": "1",
"size": "4442",
"license": "mit",
"hash": -7012186850743243000,
"line_mean": 31.6617647059,
"line_max": 111,
"alpha_frac": 0.6969833408,
"autogenerated": false,
"ratio": 2.967267869071476,
"config_test": f... |
from .fgir import *
from .optimize import FlowgraphOptimization
from .error import Warn
import asyncio
class PCodeOp(object):
'''A class interface for creating coroutines.
This helps us keep track of valid computational elements. Every coroutine in
a PCode object should be an method of PCodeOp.'''
@staticmet... | {
"repo_name": "cs207-project/pype-package",
"path": "pype/pcode.py",
"copies": "1",
"size": "5336",
"license": "mit",
"hash": 8957061547037572000,
"line_mean": 33.8758169935,
"line_max": 113,
"alpha_frac": 0.6615442279,
"autogenerated": false,
"ratio": 3.372945638432364,
"config_test": false,
... |
from .fgir import *
from .optimize import FlowgraphOptimization
from .error import Warn
import asyncio
class PCodeOp(object):
'''
A class interface for creating coroutines.
This helps us keep track of valid computational elements. Every coroutine
in a PCode object should be an method of PCodeOp.
... | {
"repo_name": "Mynti207/cs207project",
"path": "pype/pcode.py",
"copies": "1",
"size": "5473",
"license": "mit",
"hash": 8518115440673274000,
"line_mean": 33.8598726115,
"line_max": 79,
"alpha_frac": 0.5729947013,
"autogenerated": false,
"ratio": 3.821927374301676,
"config_test": false,
"has_... |
# from fgmetric.shell.FGDatabase import FGDatabase
import sys
from pprint import pprint
# from fgmetric.shell.FGSearch import FGSearch
# from fgmetric.shell.FGInstances import FGInstances
class CMMetricAPI:
""" CloudMesh Metric Python API
This API supports usage statistics in CM Metric way, but rely on data... | {
"repo_name": "rajpushkar83/cloudmesh",
"path": "cloudmesh/metric/api/old/cm_metric_api.py",
"copies": "1",
"size": "6689",
"license": "apache-2.0",
"hash": -3822951395890636300,
"line_mean": 29.1306306306,
"line_max": 109,
"alpha_frac": 0.5905217521,
"autogenerated": false,
"ratio": 4.0514839491... |
from FgzWriter import FgzWriter
from ..files import Dir
class FgzPacker:
def __init__(self,working_dir,target_dir):
self.m_workingDir=[]
for d in working_dir:
self.m_workingDir.append(Dir.Dir_toStdName(d))
self.m_targetDir=Dir.Dir_toStdName(target_dir)
self.m_packageName="fdata.fgz"
self.m_ignoreFileEx... | {
"repo_name": "FSource/Faeris",
"path": "tool/binpy/libpy/data/FgzPacker.py",
"copies": "1",
"size": "1409",
"license": "mit",
"hash": -4576589642520983600,
"line_mean": 17.2987012987,
"line_max": 62,
"alpha_frac": 0.699787083,
"autogenerated": false,
"ratio": 2.7412451361867705,
"config_test":... |
from fhirtordf.rdfsupport.namespaces import FHIR
from i2fhirb2.fhir.fhirspecific import DEFAULT_SOURCE_SYSTEM, DEFAULT_BASE_PATH
from i2b2model.sqlsupport.dbconnection import FileAwareParser
from i2fhirb2 import __version__
def add_common_parameters(parser: FileAwareParser, multi_upload_ids: bool=False) -> FileAware... | {
"repo_name": "BD2KOnFHIR/i2FHIRb2",
"path": "i2fhirb2/common_cli_parameters.py",
"copies": "1",
"size": "2193",
"license": "apache-2.0",
"hash": -2166669515380953900,
"line_mean": 58.2702702703,
"line_max": 117,
"alpha_frac": 0.6762425901,
"autogenerated": false,
"ratio": 4.03125,
"config_test... |
from FIAT.hdivcurl import Hdiv, Hcurl
from FIAT.reference_element import LINE
import gem
from gem.utils import cached_property
from finat.finiteelementbase import FiniteElementBase
from finat.tensor_product import TensorProductElement
class WrapperElementBase(FiniteElementBase):
"""Common base class for H(div) a... | {
"repo_name": "FInAT/FInAT",
"path": "finat/hdivcurl.py",
"copies": "1",
"size": "7471",
"license": "mit",
"hash": 7811075837135823000,
"line_mean": 35.6225490196,
"line_max": 82,
"alpha_frac": 0.6264221657,
"autogenerated": false,
"ratio": 3.8931735278791035,
"config_test": false,
"has_no_ke... |
from FIAT.polynomial_set import mis
from FIAT.reference_element import LINE
import gem
from gem.utils import cached_property
from finat.finiteelementbase import FiniteElementBase
class RuntimeTabulated(FiniteElementBase):
"""Element placeholder for tabulations provided at run time through a
kernel argument.... | {
"repo_name": "FInAT/FInAT",
"path": "finat/runtime_tabulated.py",
"copies": "1",
"size": "3710",
"license": "mit",
"hash": 3365151722648675000,
"line_mean": 32.7272727273,
"line_max": 98,
"alpha_frac": 0.5797843666,
"autogenerated": false,
"ratio": 4.774774774774775,
"config_test": false,
"h... |
from fiber_properties import FiberImage, circle_array, gaussian_array, show_image_array, plot_cross_sections
import numpy as np
import matplotlib.pyplot as plt
def testMethod(method):
tol = 1
test_range = 1
factor = 1.0
y_err = []
x_err = []
d_err = []
for i in xrange... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/center_accuracy.py",
"copies": "2",
"size": "4249",
"license": "mit",
"hash": 3852799347895720000,
"line_mean": 34.9652173913,
"line_max": 118,
"alpha_frac": 0.5375382443,
"autogenerated": false,
"ratio": 3.2584355828220857,
"config_... |
from fiber_properties import FiberImage, image_list
import imageio
def main():
num_images = 1
interval = 1
max_image = 10
cam = 'nf'
# folder = '../data/modal_noise/Kris_data/rectangular_100x300um/baseline/'
# folder = '../data/modal_noise/rv_error/coupled_ag_new/'
folder = '../da... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/gif_maker.py",
"copies": "2",
"size": "1212",
"license": "mit",
"hash": -5404772365878374000,
"line_mean": 37.1612903226,
"line_max": 97,
"alpha_frac": 0.5792079208,
"autogenerated": false,
"ratio": 3.2493297587131367,
"config_test":... |
from fiber_properties import (FiberImage, modal_noise, plot_fft, show_plots,
save_plot, show_image_array, image_list)
import numpy as np
import re
FOLDER = '../data/EXPRES/rectangular_132/modal_noise/'
CAMS = ['nf', 'ff']
METHOD = 'rectangle'
if __name__ == '__main__':
for ... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/EXPRES_modal_noise.py",
"copies": "2",
"size": "1139",
"license": "mit",
"hash": 6310114675846053000,
"line_mean": 34.7419354839,
"line_max": 76,
"alpha_frac": 0.4635645303,
"autogenerated": false,
"ratio": 3.9275862068965517,
"confi... |
from fiber_properties import FiberImage, plot_modal_noise, show_plots, save_plot
from modal_noise_script import save_modal_noise_data, object_file
import numpy as np
METHOD = 'filter'
CAMS = ['nf', 'ff']
CASE = 1
FOLDER = "C:/Libraries/Box Sync/ExoLab/Fiber_Characterization/Image Analysis/data/modal_noise/"
... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/modal_noise_comparison.py",
"copies": "2",
"size": "4609",
"license": "mit",
"hash": -5621480158849720000,
"line_mean": 39.5225225225,
"line_max": 142,
"alpha_frac": 0.53156867,
"autogenerated": false,
"ratio": 2.98124191461837,
"con... |
from fiber_properties import FiberImage
import matplotlib.pyplot as plt
import os
import numpy as np
PLOT_FIBER_CENTROID = True
NEW_DATA = False
NUM_IMAGES = 1
CASE = 3
FOLDER = '/Users/Dominic/Box Sync/Fiber_Characterization/Image Analysis/data/modal_noise/rv_error/'
CAMERAS = ['nf', 'ff']
METHOD = 'edge'
... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/modal_noise_rv_error.py",
"copies": "2",
"size": "9737",
"license": "mit",
"hash": 944863127379981000,
"line_mean": 47.9333333333,
"line_max": 308,
"alpha_frac": 0.5029269796,
"autogenerated": false,
"ratio": 3.106892150606254,
"conf... |
from FiberProperties import ImageAnalysis, Calibration
base_folder = 'Stability Measurements/2016-08-15 Stability Test Unagitated/'
ambient_folder = base_folder + 'Ambient/'
dark_folder = base_folder + 'Dark/'
flat_folder = base_folder + 'Flat/'
folder = base_folder + 'Images/'
ext = '.fit'
in_calibration = ... | {
"repo_name": "rpetersburg/fiber_properties",
"path": "code_testing/Kernel Test.py",
"copies": "2",
"size": "1145",
"license": "mit",
"hash": 1881650875246190600,
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"line_max": 106,
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"autogenerated": false,
"ratio": 3.2072829131652663,
"confi... |
from FiberProperties import ImageAnalysis
#nf_images = ["../Alignment Images/2016-06-30/nf_sn67_0.30ms.tif",
# "../Alignment Images/2016-06-30/nf_sn67_0.09ms.tif",
# "../Alignment Images/2016-06-30/nf_sn67_2.00ms.tif",
# "../Alignment Images/2016-06-30/nf_sn67_5.00ms.tif",
# ... | {
"repo_name": "rpetersburg/fiber_properties",
"path": "code_testing/Tophat Test.py",
"copies": "2",
"size": "1386",
"license": "mit",
"hash": 2614507600967620600,
"line_mean": 43.7419354839,
"line_max": 95,
"alpha_frac": 0.6536796537,
"autogenerated": false,
"ratio": 2.967880085653105,
"config_... |
from fiber_properties import (scrambling_gain, image_list,
plot_scrambling_gain_input_output,
plot_scrambling_gain, save_plot, show_plots,
load_image_object, FiberImage)
if __name__ == '__main__':
NEW_DATA = False
... | {
"repo_name": "rpetersburg/FiberProperties",
"path": "scripts/scrambling_gain.py",
"copies": "2",
"size": "2704",
"license": "mit",
"hash": 710394892491945600,
"line_mean": 45.4736842105,
"line_max": 102,
"alpha_frac": 0.5972633136,
"autogenerated": false,
"ratio": 3.1515151515151514,
"config_t... |
from FibonacciHeap import FibHeap
from priority_queue import FibPQ, HeapPQ, QueuePQ
def solve(maze):
# Width is used for indexing, total is used for array sizes
width = maze.width
total = maze.width * maze.height
# Start node, end node
start = maze.start
startpos = start.Position
end = maz... | {
"repo_name": "mikepound/mazesolving",
"path": "dijkstra.py",
"copies": "1",
"size": "4199",
"license": "unlicense",
"hash": -3187373109151298000,
"line_mean": 38.2429906542,
"line_max": 149,
"alpha_frac": 0.5908549655,
"autogenerated": false,
"ratio": 4.149209486166008,
"config_test": false,
... |
from FibonacciHeap import FibHeap
from priority_queue import FibPQ, HeapPQ, QueuePQ
# This implementatoin of A* is almost identical to the Dijkstra implementation. So for clarity I've removed all comments, and only added those
# Specifically showing the difference between dijkstra and A*
def solve(maze):
width = ... | {
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"path": "astar.py",
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"ha... |
from fibonacci_heap import FibonacciHeap
from bellman_ford_algorithm import *
# key is assigned dist[u], value is assigned u
class Data:
def __init__(self, key, value):
self.key = key
self.value = value
def __lt__(self, other):
# print 'lt', self.key, other.key
return self.key... | {
"repo_name": "CheYulin/MSBD5009",
"path": "python_playground/dijstra_algorithm.py",
"copies": "1",
"size": "3585",
"license": "mit",
"hash": 4150315897237211600,
"line_mean": 29.1260504202,
"line_max": 80,
"alpha_frac": 0.5165969317,
"autogenerated": false,
"ratio": 3.3011049723756907,
"config... |
from fibonacci_message_encoding import algorithms, errors
def main():
print("Choices:")
print("\t 1: Encode custom secret")
print("\t 2: Encode \"william\"")
choice = eval(input("Choice: "))
if choice == 1:
custom_encode()
elif choice == 2:
print("Encoding \"william\" as a li... | {
"repo_name": "Telkkar/fibonacci_message_encoding",
"path": "fibonacci_message_encoding/main.py",
"copies": "1",
"size": "1164",
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"hash": -445542703331070700,
"line_mean": 26.7380952381,
"line_max": 98,
"alpha_frac": 0.6159793814,
"autogenerated": false,
"ratio": 3.82894736842105... |
from fibonacci_series import FIBONACCI_SERIES
import unittest
class Fibonacci:
def fibonacci_generator(n):
if n < 1:
raise ValueError("n must be at least 1")
a, b = 0, 1
yield a
yield b
while True:
a, b = b, a + b
yield b
def fibona... | {
"repo_name": "pieteradejong/joie-de-code",
"path": "fibonacci/fibonacci.py",
"copies": "1",
"size": "2359",
"license": "mit",
"hash": -4223345600781203000,
"line_mean": 24.0957446809,
"line_max": 77,
"alpha_frac": 0.5553200509,
"autogenerated": false,
"ratio": 2.828537170263789,
"config_test":... |
from fibonnachi_compute import fibonacci_generator
import socket
import threading
class FibonnachiServer(threading.Thread):
def __init__(self, this_host, this_port, this_socket):
threading.Thread.__init__(self)
self.host = this_host
self.port = this_port
self.socket = this_socket
... | {
"repo_name": "cmavromichalis/PythonFibb",
"path": "socket_server.py",
"copies": "1",
"size": "2411",
"license": "mit",
"hash": -4454150033193991700,
"line_mean": 36.0923076923,
"line_max": 79,
"alpha_frac": 0.5180423061,
"autogenerated": false,
"ratio": 4.267256637168142,
"config_test": false,... |
from fibpro.rpc import Client, Server, DynamicObject
from fibpro.config import USER_DB_FILE
from fibpro.util import load_config
from fibpro.logsink import LogSinkClient
class UserStoreBase(object):
NAME = "userstore"
LOG_RPC = True
class UserStoreServer(UserStoreBase, Server):
def server_init(self):
... | {
"repo_name": "neumark/practical-microservices",
"path": "fibpro/userstore.py",
"copies": "1",
"size": "2369",
"license": "apache-2.0",
"hash": 7760238246252071000,
"line_mean": 31.4520547945,
"line_max": 68,
"alpha_frac": 0.6146053187,
"autogenerated": false,
"ratio": 3.8963815789473686,
"conf... |
from fibpro.rpc import GenericClient, Server
from fibpro.logsink import LogSinkClient
from fibpro.pricing import PricingClient
class ControllerBase(object):
NAME = "controller"
LOG_RPC = True
class ControllerServer(ControllerBase, Server):
def server_init(self):
self.log = LogSinkClient(self.serv... | {
"repo_name": "neumark/practical-microservices",
"path": "fibpro/controller.py",
"copies": "1",
"size": "1468",
"license": "apache-2.0",
"hash": -7329747382482672000,
"line_mean": 38.6756756757,
"line_max": 98,
"alpha_frac": 0.6607629428,
"autogenerated": false,
"ratio": 3.5980392156862746,
"co... |
from ficloud.fig_ext import transform_config
def test_transform_config():
config = {'redis': {'image': 'orchardup/redis'},
'web': {'build': '.',
'links': ['redis'],
'volumes': ['.:/code'],
'~dev': {'ports': ['5000:6000']},
... | {
"repo_name": "pywizard/ficloud",
"path": "tests/test_fig_ext.py",
"copies": "1",
"size": "1526",
"license": "apache-2.0",
"hash": -8765884784988095000,
"line_mean": 24.4333333333,
"line_max": 84,
"alpha_frac": 0.3866317169,
"autogenerated": false,
"ratio": 3.815,
"config_test": true,
"has_no... |
from fido2 import cbor
from fido2.client import ClientData
from fido2.ctap2 import AuthenticatorData
from flask import abort, current_app, flash, redirect, request, session, url_for
from flask_login import current_user
from werkzeug.exceptions import Forbidden
from app.main import main
from app.models.user import User... | {
"repo_name": "alphagov/notifications-admin",
"path": "app/main/views/webauthn_credentials.py",
"copies": "1",
"size": "7238",
"license": "mit",
"hash": -6058881370458852000,
"line_mean": 39.6516853933,
"line_max": 118,
"alpha_frac": 0.6980375898,
"autogenerated": false,
"ratio": 3.93474714518760... |
from .field import BaseField
from collections import OrderedDict
from .prepareable import Prepareable
from .class_factory import ClassFactory, default_class_factory
import six
class JavaClassMeta(six.with_metaclass(Prepareable, type)):
def __prepare__(name, bases):
return OrderedDict()
def __new__(se... | {
"repo_name": "lodevil/javaobject",
"path": "javaobject/java/javacls.py",
"copies": "1",
"size": "2288",
"license": "bsd-3-clause",
"hash": 4429351967380027400,
"line_mean": 31.6857142857,
"line_max": 80,
"alpha_frac": 0.5533216783,
"autogenerated": false,
"ratio": 4.268656716417911,
"config_te... |
from .Field import Field, FieldElement
class ComplexField(Field):
def __init__(s):
super(ComplexField, s).__init__(ComplexFieldElement)
def _add(s, a, b):
return s.element_class(s, a[0] + b[0], a[1] + b[1])
def _neg(s, a):
return s.element_class(s, -a[0], -a[1])
def _mul(s, a, b):
return s.... | {
"repo_name": "elliptic-shiho/ecpy",
"path": "ecpy/fields/ComplexField.py",
"copies": "1",
"size": "1736",
"license": "mit",
"hash": 5809369506348026000,
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"line_max": 71,
"alpha_frac": 0.4873271889,
"autogenerated": false,
"ratio": 2.674884437596302,
"config_test": fa... |
from .field import Field
from .exceptions import BadValidation
class Form(object):
def __init__(self, data=None):
self.fields, self.forms = self._get_fields_and_nested_forms()
self._initialize_data(data)
self.errors = {}
self.valid = None
def _get_fields_and_nested_forms(self... | {
"repo_name": "hugollm/lie2me",
"path": "lie2me/form.py",
"copies": "1",
"size": "2014",
"license": "mit",
"hash": -5909899488154847000,
"line_mean": 28.6176470588,
"line_max": 79,
"alpha_frac": 0.5163853029,
"autogenerated": false,
"ratio": 4.2133891213389125,
"config_test": false,
"has_no_k... |
from ..field import Field
from ..exceptions import ValidationError, BadConfiguration
class Dict(Field):
def __init__(self, fields=None, *args, **kwargs):
self.fields = self._validate_fields(fields)
super().__init__(*args, **kwargs)
def _validate_fields(self, fields):
if fields is Non... | {
"repo_name": "hugollm/lie2me",
"path": "lie2me/fields/dict.py",
"copies": "1",
"size": "1193",
"license": "mit",
"hash": 7378428253952009000,
"line_mean": 27.4047619048,
"line_max": 91,
"alpha_frac": 0.5507124895,
"autogenerated": false,
"ratio": 4.468164794007491,
"config_test": false,
"has... |
from ..field import Field
from ..parsers import parse_datetime
class DateTime(Field):
timezone = None
min = None
max = None
messages = {
'type': 'Invalid date or time.',
'naive': 'Requires timezone information.',
'aware': 'Must not have timezone information.',
'min': ... | {
"repo_name": "hugollm/lie2me",
"path": "lie2me/fields/datetime.py",
"copies": "1",
"size": "1345",
"license": "mit",
"hash": 3845297760035133400,
"line_mean": 32.625,
"line_max": 72,
"alpha_frac": 0.5888475836,
"autogenerated": false,
"ratio": 4.164086687306502,
"config_test": false,
"has_no... |
from ..field import Field
from ..parsers import parse_time
class Time(Field):
timezone = None
min = None
max = None
messages = {
'type': 'Invalid time.',
'naive': 'Requires timezone information.',
'aware': 'Must not have timezone information.',
'min': 'Must not come b... | {
"repo_name": "hugollm/lie2me",
"path": "lie2me/fields/time.py",
"copies": "1",
"size": "1309",
"license": "mit",
"hash": -4906052081943997000,
"line_mean": 31.725,
"line_max": 68,
"alpha_frac": 0.5790679908,
"autogenerated": false,
"ratio": 4.077881619937695,
"config_test": false,
"has_no_ke... |
from .field import Field
class Dict(Field):
"""
A dict of values.
:param schema: class to be used for validation/serialization of the
incoming values
:type schema: dict_validator.Schema
"""
def __init__(self, schema, *args, **kwargs):
super(Dict, self).__init__(*args, **kwarg... | {
"repo_name": "gurunars/dict-validator",
"path": "dict_validator/dict_field.py",
"copies": "1",
"size": "2268",
"license": "mit",
"hash": -8857297595055092000,
"line_mean": 33.8923076923,
"line_max": 77,
"alpha_frac": 0.5401234568,
"autogenerated": false,
"ratio": 4.378378378378378,
"config_tes... |
from _field import *
from _field import __doc__
import numpy as np
import graph as fg
"""
This module implements the field structure of nipy.neurospin
Author:Bertrand Thirion, 2006--2009
Fixme : add a subfield method, similar to subgraph
"""
class Field(fg.WeightedGraph):
"""
This is the basic field structu... | {
"repo_name": "yarikoptic/NiPy-OLD",
"path": "nipy/neurospin/graph/field.py",
"copies": "1",
"size": "15636",
"license": "bsd-3-clause",
"hash": 6410828138811609000,
"line_mean": 34.5363636364,
"line_max": 103,
"alpha_frac": 0.5253261704,
"autogenerated": false,
"ratio": 4.082506527415144,
"con... |
from field import *
from os import mkdir, listdir
from creatures import Creature
from tweaks import log as LOG
log = lambda *x: LOG(*x, f='logs/mapgen.log')
class BigMap:
'''
The class keeps __all__ the data about current 'level'; that includes:
-- Every cell's floor and fill materials (done)
... | {
"repo_name": "untergrunt/untergrunt",
"path": "mapgen.py",
"copies": "1",
"size": "10469",
"license": "mit",
"hash": -1105819941530843900,
"line_mean": 41.0441767068,
"line_max": 161,
"alpha_frac": 0.4493265832,
"autogenerated": false,
"ratio": 3.2371675943104514,
"config_test": false,
"has_... |
from Field import *
from Item import *
from MainGame import *
from Monster import *
from NPC import *
from Player import *
from quest import*
from Drawing import*
import math
import tkinter
class MainGame:
def __init__(self):
self.started = 0
self.qposes = []
self.curquests =... | {
"repo_name": "pindos11/tkinter-roguelike",
"path": "MainGame.py",
"copies": "1",
"size": "24155",
"license": "mit",
"hash": 709425894241124600,
"line_mean": 39.5748709122,
"line_max": 116,
"alpha_frac": 0.4684330366,
"autogenerated": false,
"ratio": 3.4311079545454546,
"config_test": false,
... |
from .field import ThumborField
from .field import ThumborData
class ThumborImageField: pass
class ThumborImageInput: pass
THUMBOR_FORMATTERS = dict()
try:
from flask import Markup,current_app
from wtforms.widgets import HTMLString, FileInput, html_params
from jinja2 import Marku... | {
"repo_name": "gerasim13/libthumbor",
"path": "libthumbor/flask/views.py",
"copies": "1",
"size": "4057",
"license": "mit",
"hash": -8884245802680285000,
"line_mean": 38.0097087379,
"line_max": 152,
"alpha_frac": 0.5756595321,
"autogenerated": false,
"ratio": 3.9586206896551723,
"config_test": ... |
from FieldPos import *
from NPC import *
import random
class Field:
def __init__(self,sizex,sizey,plr,ztype):
self.vil=plr
self.ztype = ztype
self.sizex = sizex
self.sizey = sizey
self.monsters = []
self.npcs = []
self.vilposes=[]
#creati... | {
"repo_name": "pindos11/tkinter-roguelike",
"path": "Field.py",
"copies": "1",
"size": "12384",
"license": "mit",
"hash": 9101240351215309000,
"line_mean": 40.2730375427,
"line_max": 97,
"alpha_frac": 0.4249031008,
"autogenerated": false,
"ratio": 3.435228848821082,
"config_test": false,
"has... |
from ..fields.chart_field import ChartField
from ..fields.title_field import TitleField
from ..fields.axis_field import AxisField
from ..fields.series_field import SeriesField
from ..fields.series.series import Series
from ..fields.color_axis_field import ColorAxisField
from collections import OrderedDict
from highchar... | {
"repo_name": "jpmfribeiro/PyCharts",
"path": "build/lib.linux-x86_64-2.7/pycharts/charts/heat_map_chart.py",
"copies": "2",
"size": "3074",
"license": "mit",
"hash": 1797262116214248200,
"line_mean": 40.5540540541,
"line_max": 121,
"alpha_frac": 0.6164606376,
"autogenerated": false,
"ratio": 3.5... |
from ..fields.chart_field import ChartField
from ..fields.title_field import TitleField
from ..fields.series_field import SeriesField
from ..fields.legend_field import LegendField
from ..fields.axis_field import AxisField
from ..fields.series.series import Series
from ..fields.plot_options_field import PlotOptionsField... | {
"repo_name": "jpmfribeiro/PyCharts",
"path": "build/lib.linux-x86_64-2.7/pycharts/charts/area_chart.py",
"copies": "2",
"size": "1667",
"license": "mit",
"hash": -9185570809273083000,
"line_mean": 36.0444444444,
"line_max": 87,
"alpha_frac": 0.6448710258,
"autogenerated": false,
"ratio": 3.42299... |
from ..fields.chart_field import ChartField
from ..fields.title_field import TitleField
from ..fields.series_field import SeriesField
from ..fields.series.series import Series
from ..fields.plot_options_field import PlotOptionsField
from ..fields.plot_options.pie_plot_options import PiePlotOptions
from highchart import... | {
"repo_name": "jpmfribeiro/PyCharts",
"path": "build/lib.linux-x86_64-2.7/pycharts/charts/pie_chart.py",
"copies": "2",
"size": "1329",
"license": "mit",
"hash": -6671525018980922000,
"line_mean": 32.225,
"line_max": 91,
"alpha_frac": 0.6260346125,
"autogenerated": false,
"ratio": 3.5534759358288... |
from fields import BaseField
from queryselectors import JasonQuerySelector
import exceptions
class JasonResourceMeta(type):
def __new__(cls, name, bases, dct):
new_class = type.__new__(cls, name, bases, dct)
# Set names to attributes
for (key, value) in new_class.__dict__.iteritems():
... | {
"repo_name": "sourcelair/jason",
"path": "jason/resources.py",
"copies": "1",
"size": "2164",
"license": "mit",
"hash": -3812658251440615400,
"line_mean": 24.7619047619,
"line_max": 69,
"alpha_frac": 0.595194085,
"autogenerated": false,
"ratio": 4.153550863723608,
"config_test": false,
"has_... |
from .fields import CharField, BooleanField, EmailField, URLField, IntegerField, DecimalField, DateTimeField, DateField, TimeField, TextField, ChoiceField
try:
from rest_framework import fields
from rest_framework import compat
except ImportError:
pass # DRF is not installed
def drf_field_to_field(drf_f... | {
"repo_name": "jonashagstedt/django-reform",
"path": "reform/drf_fields.py",
"copies": "1",
"size": "1776",
"license": "bsd-3-clause",
"hash": 5820939665116483000,
"line_mean": 30.1578947368,
"line_max": 154,
"alpha_frac": 0.6773648649,
"autogenerated": false,
"ratio": 4.238663484486874,
"confi... |
from .fields import ErrorSchema, Error, UserSchema
from flask import jsonify
def get_error_json(message="There is an error.", code=400, additional_errors=[]):
error_schema = ErrorSchema(many=False)
error = Error(message=message, code=code, errors=additional_errors)
return error_schema.dump(error).data, cod... | {
"repo_name": "Arkanayan/BuieConnect-Web",
"path": "app/api/v1/resources/utils.py",
"copies": "1",
"size": "1101",
"license": "mit",
"hash": -1631733561162991400,
"line_mean": 27.2307692308,
"line_max": 81,
"alpha_frac": 0.6702997275,
"autogenerated": false,
"ratio": 3.8767605633802815,
"config... |
from .fields import Field
from .exceptions import ConsoleError
import argparse
import random
def prompt(field):
"""
method to prompt and return user input.
:field instance of registrar.Field
"""
try:
if field.field_type==Field.TYPE_NONE:
print field.field_name
retur... | {
"repo_name": "gorbinphilip/PyRegistrar",
"path": "pyregistrar/console.py",
"copies": "1",
"size": "1618",
"license": "mit",
"hash": -594773006151784100,
"line_mean": 34.1739130435,
"line_max": 125,
"alpha_frac": 0.6131025958,
"autogenerated": false,
"ratio": 4.596590909090909,
"config_test": f... |
from .fields import FIELD_NO_INPUT
import gc
import json
def run_all(rule_list,
defined_variables,
defined_actions,
stop_on_first_trigger=False):
rule_was_triggered = False
for rule in rule_list:
result = run(rule, defined_variables, defined_actions)
if resul... | {
"repo_name": "hubertokf/lupsContextServer",
"path": "EngineRules/build/lib/business_rules/engine.py",
"copies": "1",
"size": "4184",
"license": "mit",
"hash": 8680639326697030000,
"line_mean": 39.5145631068,
"line_max": 200,
"alpha_frac": 0.6374311047,
"autogenerated": false,
"ratio": 4.38340336... |
from .fields import FIELD_NO_INPUT
def run_all(rule_list,
defined_variables,
defined_actions,
defined_operators=None,
stop_on_first_trigger=False):
rule_was_triggered = False
for rule in rule_list:
result = run(rule, defined_variables, defined_actions, d... | {
"repo_name": "adnymics/business-rules",
"path": "business_rules/engine.py",
"copies": "1",
"size": "4688",
"license": "mit",
"hash": 7522258500110796000,
"line_mean": 41.2342342342,
"line_max": 100,
"alpha_frac": 0.6606228669,
"autogenerated": false,
"ratio": 4.512030798845044,
"config_test": ... |
from .fields import FIELD_NO_INPUT
def run_all(rule_list,
defined_variables,
defined_actions,
stop_on_first_trigger=False):
rule_was_triggered = False
for rule in rule_list:
result = run(rule, defined_variables, defined_actions)
if result:
rule_w... | {
"repo_name": "erikdejonge/business-rules",
"path": "business_rules/engine.py",
"copies": "3",
"size": "3731",
"license": "mit",
"hash": 2963141834520334300,
"line_mean": 37.8645833333,
"line_max": 82,
"alpha_frac": 0.6531760922,
"autogenerated": false,
"ratio": 4.399764150943396,
"config_test"... |
from fields import Field, RelatedObject, _novalue
from stdnet.exceptions import *
from stdnet import pipelines
from stdnet.utils import ModelFieldPickler
class MultiField(Field):
'''Virtual class for data-structure fields:
* *model* optional :ref:`StdModel <model-model>` class.
* *related... | {
"repo_name": "TheProjecter/python-stdnet",
"path": "stdnet/orm/std.py",
"copies": "1",
"size": "8009",
"license": "bsd-3-clause",
"hash": 1053932743170723700,
"line_mean": 32.670995671,
"line_max": 134,
"alpha_frac": 0.5433886877,
"autogenerated": false,
"ratio": 4.25557917109458,
"config_test... |
from .fields import get_field_type
from .fields._field import Field
from ._table import Table
from ._view import View
class Schema(object):
@property
def schema_name(self):
return self._name
@property
def fields(self):
if self._fields is None:
self._fields = []
... | {
"repo_name": "artPlusPlus/punkin",
"path": "punkin/_schema.py",
"copies": "1",
"size": "1501",
"license": "mit",
"hash": -104256284738623220,
"line_mean": 28.431372549,
"line_max": 65,
"alpha_frac": 0.5862758161,
"autogenerated": false,
"ratio": 4.101092896174864,
"config_test": false,
"has_... |
from .fields import ListField,IntegerField
from .base import Element
from .fields import ChoiceField,StringField
__all__ = ['Button']
class Button(Element):
tag = "button"
type = ChoiceField({"submit", "reset", "primary"}, default="submit")
icon = StringField(required=False)
allow_text = True
de... | {
"repo_name": "vivsh/kaya",
"path": "src/kaya/tags/forms.py",
"copies": "1",
"size": "5677",
"license": "mit",
"hash": 8168311723657902000,
"line_mean": 26.5582524272,
"line_max": 93,
"alpha_frac": 0.5150607715,
"autogenerated": false,
"ratio": 3.901718213058419,
"config_test": false,
"has_no... |
from .fields import ListField, SetField, DictField, EmbeddedModelField
from django.db import models, connections
from django.db.models import Q
from django.db.models.signals import post_save
from django.db.utils import DatabaseError
from django.dispatch.dispatcher import receiver
from django.test import TestCase
from d... | {
"repo_name": "bmander/dancecontraption",
"path": "djangotoolbox/tests.py",
"copies": "11",
"size": "12305",
"license": "bsd-3-clause",
"hash": -4162415782370402000,
"line_mean": 41.2852233677,
"line_max": 89,
"alpha_frac": 0.6034132466,
"autogenerated": false,
"ratio": 3.7606968215158925,
"con... |
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