text stringlengths 0 1.05M | meta dict |
|---|---|
from dbfUtils import *
from struct import unpack
from math import *
from random import uniform, random
def load_shape(shapefile):
global records
global record_dict
record_dict = {}
records = open(shapefile, mode='rb')
#unpack the header
header1 = unpack('>7i', records.read(28))
header2 = unpack('<2i', recor... | {
"repo_name": "stahlba2/Python-Shapefile-Reader",
"path": "shpread.py",
"copies": "1",
"size": "26026",
"license": "mit",
"hash": -6025971606697499000,
"line_mean": 32.0698856417,
"line_max": 222,
"alpha_frac": 0.6272957811,
"autogenerated": false,
"ratio": 2.5051496775435558,
"config_test": fa... |
__author__ = 'brunocatao'
from django.http import HttpResponseRedirect
from django.core.urlresolvers import reverse
from django.shortcuts import get_object_or_404
from django.views.decorators.csrf import csrf_protect
from django.contrib.auth.decorators import login_required
from django.views.generic.simple import dire... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/files/views.py",
"copies": "1",
"size": "2219",
"license": "bsd-3-clause",
"hash": -2240706787319636700,
"line_mean": 31.6470588235,
"line_max": 102,
"alpha_frac": 0.7016674178,
"autogenerated": false,
"ratio": 3.754653130287648,
... |
__author__ = 'brunocatao'
from django import forms
from django.contrib.contenttypes.models import ContentType
from django.utils.encoding import force_unicode
from django.utils.translation import ugettext_lazy as _
from portal.messages.models import Message, Attachment
class MessageForm(forms.Form):
content_type ... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/messages/forms.py",
"copies": "1",
"size": "1919",
"license": "bsd-3-clause",
"hash": 212428114262167700,
"line_mean": 39,
"line_max": 159,
"alpha_frac": 0.6399166232,
"autogenerated": false,
"ratio": 4.006263048016701,
"config_te... |
__author__ = 'brunocatao'
from django import forms
from django.utils.translation import ugettext as _
from django.contrib.auth.models import User
from portal.models import UserInfo
class RegisterUserForm(forms.Form):
email = forms.EmailField(label=_('Email'), required=True, max_length=100)
passwor... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/accounts/forms.py",
"copies": "1",
"size": "1471",
"license": "bsd-3-clause",
"hash": -3688378505504465000,
"line_mean": 35.8,
"line_max": 132,
"alpha_frac": 0.6498980286,
"autogenerated": false,
"ratio": 4.0974930362116995,
"conf... |
__author__ = 'brunocatao'
from django import forms
from django.utils.translation import ugettext as _
from portal.models import Institution
from portal.constants import STATES_CHOICES
class InstitutionForm(forms.ModelForm):
name = forms.CharField(label=_('Name'), required=True, max_length=100)
acrony... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/institutions/forms.py",
"copies": "1",
"size": "1436",
"license": "bsd-3-clause",
"hash": -1120634822723354100,
"line_mean": 50.3214285714,
"line_max": 105,
"alpha_frac": 0.680362117,
"autogenerated": false,
"ratio": 3.8810810810810... |
__author__ = 'brunocatao'
from django.test import TestCase
from google.appengine.ext import db
from google.appengine.api import images
import logging
from portal.models import Picture
class PictureTestCase(TestCase):
PICTURE_FILE_NAME = '/Users/brunocatao/Pictures/foto.jpg'
def setUp(self):
logging.i... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/tests.py",
"copies": "1",
"size": "2328",
"license": "bsd-3-clause",
"hash": -5928493114983545000,
"line_mean": 34.2878787879,
"line_max": 88,
"alpha_frac": 0.6842783505,
"autogenerated": false,
"ratio": 3.8543046357615895,
"confi... |
__author__ = 'brunocatao'
import datetime
from django.contrib.auth.models import User
from django.db import models
from django.contrib.contenttypes import generic
from django.contrib.contenttypes.models import ContentType
from django.utils.encoding import force_unicode
from django.utils.translation import ugettext as ... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/files/models.py",
"copies": "1",
"size": "1737",
"license": "bsd-3-clause",
"hash": 3967593584850374700,
"line_mean": 43.5641025641,
"line_max": 130,
"alpha_frac": 0.7023603915,
"autogenerated": false,
"ratio": 3.868596881959911,
... |
__author__ = 'brunocatao'
import datetime
from django.db import models
from django.contrib.contenttypes import generic
from django.contrib.contenttypes.models import ContentType
from django.utils.encoding import force_unicode
from django.utils.translation import ugettext as _
from django.contrib.auth.models import Use... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/updates/models.py",
"copies": "1",
"size": "5156",
"license": "bsd-3-clause",
"hash": 1362164682577250300,
"line_mean": 36.6423357664,
"line_max": 130,
"alpha_frac": 0.5812645462,
"autogenerated": false,
"ratio": 4.174898785425102,
... |
__author__ = 'brunocatao'
import random
import datetime
from django.db import models
from django.contrib.contenttypes import generic
from django.contrib.contenttypes.models import ContentType
from django.utils.encoding import force_unicode
from django.utils.translation import ugettext as _
from portal.models import P... | {
"repo_name": "brunogamacatao/portalsaladeaula",
"path": "portal/album/models.py",
"copies": "1",
"size": "3361",
"license": "bsd-3-clause",
"hash": 5031773764010853000,
"line_mean": 39.5060240964,
"line_max": 130,
"alpha_frac": 0.6902707528,
"autogenerated": false,
"ratio": 3.742761692650334,
... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
__all__ = [
"call_external",
]
import Queue as queue
import multiprocessing
import cargo
class CallProcess(multiprocessing.Process):
def __init__(self, method, to_master):
self._method = method
self._to_master = to_master
def run(... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/concurrent.py",
"copies": "1",
"size": "1539",
"license": "mit",
"hash": -3794571331515227000,
"line_mean": 23.8225806452,
"line_max": 66,
"alpha_frac": 0.5964912281,
"autogenerated": false,
"ratio": 3.7813267813267815,
"config_test"... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
from __future__ import absolute_import
import os
import sys
import time
import zlib
import socket
import signal
import random
import traceback
import subprocess
import collections
import multiprocessing
import cPickle as pickle
import numpy
import cargo
logger = ... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/labor2.py",
"copies": "1",
"size": "13097",
"license": "mit",
"hash": 181364940394797380,
"line_mean": 26.3423799582,
"line_max": 100,
"alpha_frac": 0.5674581965,
"autogenerated": false,
"ratio": 4.331018518518518,
"config_test": fal... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
from __future__ import absolute_import
import plac
import os.path
import imp
import uuid
import borg.log
logger = borg.log.get_logger(__name__, default_level = "INFO")
named_domains = {}
def do(*args, **kwargs):
import condor
return condor.do(*args, **k... | {
"repo_name": "borg-project/borg",
"path": "borg/__init__.py",
"copies": "1",
"size": "2668",
"license": "mit",
"hash": -6010911084151923000,
"line_mean": 21.8034188034,
"line_max": 75,
"alpha_frac": 0.6604197901,
"autogenerated": false,
"ratio": 3.6348773841961854,
"config_test": false,
"has... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import contextlib
import borg
from . import instance
from . import solvers
from . import features
from . import test
logger = borg.get_logger(__name__, default_level = "INFO")
class MAX_SAT_Task(object):
def __init__(self, path):
self.path = path
... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/max_sat/__init__.py",
"copies": "1",
"size": "1359",
"license": "mit",
"hash": -3087217322101146600,
"line_mean": 21.2786885246,
"line_max": 58,
"alpha_frac": 0.5783664459,
"autogenerated": false,
"ratio": 3.7960893854748603,
"config_test... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import contextlib
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "DETAIL")
class FakeSolverProcess(object):
"""Provide a solver interface to stored run data."""
def __init__(self, run):
"""Initialize."""
self... | {
"repo_name": "borg-project/borg",
"path": "borg/fake.py",
"copies": "1",
"size": "3592",
"license": "mit",
"hash": 306313750521106100,
"line_mean": 23.9444444444,
"line_max": 105,
"alpha_frac": 0.5815701559,
"autogenerated": false,
"ratio": 4.091116173120729,
"config_test": false,
"has_no_ke... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import cStringIO as StringIO
import nose
import nose.tools
import borg
input_text_plain = \
"""* foo!@#$
-1 x1 +23 x2 = +0;
-1 x1 +23 x2 >= -0;
-1 x1 +23 x2 >= -1;
* foo!@#$
+1 x1 >= 42 ;
* foo!@#$
* foo!@#$
"""
input_text_nlc = \
"""* foo!@#$
-1 x1 x2 +23 x2... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/pb/test/test_opb.py",
"copies": "1",
"size": "2191",
"license": "mit",
"hash": -5614035782577130000,
"line_mean": 27.8289473684,
"line_max": 75,
"alpha_frac": 0.5600182565,
"autogenerated": false,
"ratio": 2.5655737704918034,
"config_test... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import csv
import itertools
import numpy
import condor
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def run_experiment(run_data, planner_name, B):
if planner_name == "knapsack":
planner = borg.planners.KnapsackPlanner()
e... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/solved_vs_b.py",
"copies": "1",
"size": "2358",
"license": "mit",
"hash": 7348667106510926000,
"line_mean": 33.1739130435,
"line_max": 95,
"alpha_frac": 0.6217133164,
"autogenerated": false,
"ratio": 3.3637660485021397,
"config_test":... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import csv
import itertools
import numpy
import sklearn
import condor
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def evaluate_features(model, testing, feature_names):
# use features
if len(feature_names) > 0:
# train th... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/ll_vs_features.py",
"copies": "1",
"size": "3462",
"license": "mit",
"hash": -2297985817542835500,
"line_mean": 37.043956044,
"line_max": 108,
"alpha_frac": 0.6013864818,
"autogenerated": false,
"ratio": 3.934090909090909,
"config_tes... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import csv
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def plan_to_start_end(category, planner_name, solver_names, plan):
t = 0
for (s, d) in plan:
yield map(str, [category, planner_name, solver_names[s], t... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/plan.py",
"copies": "1",
"size": "3363",
"license": "mit",
"hash": -2255981617776169500,
"line_mean": 31.0285714286,
"line_max": 90,
"alpha_frac": 0.5783526613,
"autogenerated": false,
"ratio": 3.319842053307009,
"config_test": false,
"ha... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import itertools
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
class RandomPortfolio(object):
"""Random portfolio."""
def __call__(self, task, suite, budget):
"""Run the portfolio."""
solvers = suite... | {
"repo_name": "borg-project/borg",
"path": "borg/portfolios.py",
"copies": "1",
"size": "7542",
"license": "mit",
"hash": 5398657686775485000,
"line_mean": 33.5963302752,
"line_max": 136,
"alpha_frac": 0.5604614161,
"autogenerated": false,
"ratio": 3.9383812010443866,
"config_test": false,
"h... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import numpy
import sklearn.svm
import sklearn.pipeline
import sklearn.linear_model
import sklearn.decomposition
import sklearn.kernel_approximation
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
class MultiClassifier(object):
def __in... | {
"repo_name": "borg-project/borg",
"path": "borg/regression.py",
"copies": "1",
"size": "4929",
"license": "mit",
"hash": 5764227892007821000,
"line_mean": 30.3949044586,
"line_max": 104,
"alpha_frac": 0.5719212822,
"autogenerated": false,
"ratio": 3.6484085862324203,
"config_test": false,
"h... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import json
import tempfile
import contextlib
import borg
logger = borg.get_logger(__name__)
def parse_clasp_json_output(stdout):
"""Parse the output from clasp."""
try:
output = json.loads(stdout)
except ValueError:
return ... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/asp/solvers.py",
"copies": "1",
"size": "3716",
"license": "mit",
"hash": 7917138542038157000,
"line_mean": 28.4920634921,
"line_max": 99,
"alpha_frac": 0.5503229279,
"autogenerated": false,
"ratio": 3.8789144050104385,
"config_test": fal... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import os.path
import csv
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
@borg.annotations(
bundle_path = ("path to new bundle",),
root_path = ("instances root directory",),
runs_extension = ("runs files extension",),... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/bundle_run_data.py",
"copies": "1",
"size": "2756",
"license": "mit",
"hash": -2323594235230234000,
"line_mean": 31.8095238095,
"line_max": 97,
"alpha_frac": 0.5845428157,
"autogenerated": false,
"ratio": 3.7142857142857144,
"config_test": ... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import os.path
import tempfile
import contextlib
import borg
from . import instance
from . import solvers
from . import features
from . import test
logger = borg.get_logger(__name__, default_level = "INFO")
class PseudoBooleanTask(object):
"""A pse... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/pb/__init__.py",
"copies": "1",
"size": "3514",
"license": "mit",
"hash": 4327076310073746000,
"line_mean": 28.5294117647,
"line_max": 110,
"alpha_frac": 0.594479226,
"autogenerated": false,
"ratio": 3.811279826464208,
"config_test": fals... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import pty
import subprocess
import borg
log = borg.get_logger(__name__)
def _child_preexec(environment):
"""Run in the child code prior to execution."""
# update the environment
for (key, value) in environment.iteritems():
os.puten... | {
"repo_name": "borg-project/borg",
"path": "borg/unix/sessions.py",
"copies": "1",
"size": "2375",
"license": "mit",
"hash": 2704219688012391000,
"line_mean": 25.3888888889,
"line_max": 82,
"alpha_frac": 0.5608421053,
"autogenerated": false,
"ratio": 3.9451827242524917,
"config_test": false,
... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import pty
import sys
import functools
import subprocess
from cargo.log import get_logger
from cargo.unix.proc import ProcessStat
from cargo.errors import Raised
log = get_logger(__name__)
def _child_preexec(environment):
"""
Run in th... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/unix/sessions.py",
"copies": "1",
"size": "2445",
"license": "mit",
"hash": 8399013479811368000,
"line_mean": 24.7368421053,
"line_max": 82,
"alpha_frac": 0.5676891616,
"autogenerated": false,
"ratio": 3.9563106796116503,
"config_tes... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import re
import datetime
class ProcFileParseError(RuntimeError):
"""A file in /proc could not be parsed."""
class ProcessStat(object):
"""
Information about a specific process.
Merely a crude wrapper around the information in the /proc... | {
"repo_name": "borg-project/borg",
"path": "borg/unix/proc.py",
"copies": "1",
"size": "8351",
"license": "mit",
"hash": -6076569407519470000,
"line_mean": 48.1235294118,
"line_max": 94,
"alpha_frac": 0.5462818824,
"autogenerated": false,
"ratio": 3.612024221453287,
"config_test": false,
"has... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import re
import tempfile
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def parse_competition(stdout):
"""Parse output from a standard competition solver."""
match = re.search(r"^s +([a-zA-Z ]+) *\r?$", std... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/pb/solvers.py",
"copies": "1",
"size": "7636",
"license": "mit",
"hash": 3490355400830166000,
"line_mean": 32.0562770563,
"line_max": 105,
"alpha_frac": 0.5449188057,
"autogenerated": false,
"ratio": 3.512419503219871,
"config_test": fals... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import signal
import multiprocessing
import condor
logger = condor.log.get_logger(__name__)
class LocalWorkerProcess(multiprocessing.Process):
"""Work in a subprocess."""
def __init__(self, stm_queue):
"""Initialize."""
multipr... | {
"repo_name": "borg-project/utcondor",
"path": "condor/managers/parallel.py",
"copies": "1",
"size": "3455",
"license": "mit",
"hash": -292769262950733400,
"line_mean": 28.5299145299,
"line_max": 104,
"alpha_frac": 0.5496382055,
"autogenerated": false,
"ratio": 4.5520421607378125,
"config_test"... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os
import socket
import condor
class Message(object):
"""Message from a worker."""
def __init__(self, sender):
self.sender = sender
self.host = socket.gethostname()
self.pid = os.getpid()
def make_summary(self, text):
... | {
"repo_name": "borg-project/utcondor",
"path": "condor/messages.py",
"copies": "1",
"size": "2326",
"license": "mit",
"hash": -1273110466965169000,
"line_mean": 25.7356321839,
"line_max": 95,
"alpha_frac": 0.5915735168,
"autogenerated": false,
"ratio": 3.7335473515248796,
"config_test": false,
... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
@borg.annotations(
suite_path = ("path to the solvers suite", "positional", None, os.path.abspath),
solver_name = ("name of solver to run", "positional"),
instance_p... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/run_for_paramils.py",
"copies": "1",
"size": "1609",
"license": "mit",
"hash": -5852073763036611000,
"line_mean": 28.7962962963,
"line_max": 89,
"alpha_frac": 0.6041019267,
"autogenerated": false,
"ratio": 3.4527896995708156,
"config_test":... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import bz2
import sys
import pwd
import gzip
import shutil
import tempfile
import json
import traceback
import contextlib
import subprocess
import numpy
def files_under(path, extensions = None):
"""Iterate over paths in the specified directory t... | {
"repo_name": "borg-project/borg",
"path": "borg/util.py",
"copies": "1",
"size": "5302",
"license": "mit",
"hash": -5290050649040553000,
"line_mean": 24.1279620853,
"line_max": 95,
"alpha_frac": 0.5976989815,
"autogenerated": false,
"ratio": 4.094208494208495,
"config_test": false,
"has_no_k... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import cStringIO as StringIO
import nose.tools
import borg
def path_to(name):
return os.path.join(os.path.dirname(__file__), name)
def test_cnf_parse_simple():
"""Test simple CNF input."""
with open(path_to("example.simple.cnf")) as cn... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/sat/test/test_instance.py",
"copies": "1",
"size": "1060",
"license": "mit",
"hash": 2714098951448984600,
"line_mean": 25.5,
"line_max": 77,
"alpha_frac": 0.6509433962,
"autogenerated": false,
"ratio": 3.0547550432276656,
"config_test": f... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import copy
import numpy
import condor
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def infer_distributions(run_data, model_name, instance, exclude):
"""Compute model predictions on every instance."""
#... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/apply_models.py",
"copies": "1",
"size": "2899",
"license": "mit",
"hash": 3924936828884834000,
"line_mean": 32.7093023256,
"line_max": 93,
"alpha_frac": 0.5998620214,
"autogenerated": false,
"ratio": 3.614713216957606,
"config_test":... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import itertools
import collections
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
class RunRecord(object):
"""Record of a solver run."""
def __init__(self, solver, budget, cost, success):
... | {
"repo_name": "borg-project/borg",
"path": "borg/storage.py",
"copies": "1",
"size": "13946",
"license": "mit",
"hash": 7673057979373106000,
"line_mean": 29.0560344828,
"line_max": 93,
"alpha_frac": 0.5539222716,
"autogenerated": false,
"ratio": 3.961931818181818,
"config_test": false,
"has_n... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
@borg.annotations(
out_root = ("results output path"),
bundle = ("path to pre-recorded runs", "positional", None, os.path.abspath),
)
def mai... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/latent_classes.py",
"copies": "1",
"size": "2156",
"license": "mit",
"hash": 6005821073727157000,
"line_mean": 27.7466666667,
"line_max": 81,
"alpha_frac": 0.6108534323,
"autogenerated": false,
"ratio": 3.389937106918239,
"config_test... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import numpy
import sklearn
import condor
import borg
import borg.experiments.simulate_runs
logger = borg.get_logger(__name__, default_level = "INFO")
def simulate_run(run, maker, all_data, train_mask, test_mask, instances, independent, ... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/simulate_iid.py",
"copies": "1",
"size": "4571",
"license": "mit",
"hash": 6725562996317923000,
"line_mean": 32.6102941176,
"line_max": 124,
"alpha_frac": 0.5491139794,
"autogenerated": false,
"ratio": 3.900170648464164,
"config_test"... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import csv
import uuid
import numpy
import sklearn
import condor
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def evaluate_split(run_data, alpha, split, train_mask, test_mask):
"""Evaluate a model on a train/test split... | {
"repo_name": "borg-project/borg",
"path": "borg/experiments/mul_over_alpha.py",
"copies": "1",
"size": "1991",
"license": "mit",
"hash": -2289466634146313500,
"line_mean": 30.6031746032,
"line_max": 87,
"alpha_frac": 0.6228026118,
"autogenerated": false,
"ratio": 3.4686411149825784,
"config_te... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import socket
import condor
logger = condor.log.get_logger(__name__, default_level = "INFO")
class DistributedManager(object):
"""Manage remotely-distributed work."""
def __init__(self, tasks, workers):
"""Initialize."""
s... | {
"repo_name": "borg-project/utcondor",
"path": "condor/managers/distributed.py",
"copies": "1",
"size": "2430",
"license": "mit",
"hash": -8957896402820144000,
"line_mean": 26.3033707865,
"line_max": 100,
"alpha_frac": 0.566255144,
"autogenerated": false,
"ratio": 4.016528925619835,
"config_tes... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import sys
import csv
import zlib
import base64
import cPickle as pickle
import numpy
import borg
import borg.distributors
logger = borg.get_logger(__name__, default_level = "INFO")
def run_solver_on(suite_path, solver_name, task_path, budget, sto... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/run_solvers.py",
"copies": "1",
"size": "4217",
"license": "mit",
"hash": 2119797261524817400,
"line_mean": 29.1214285714,
"line_max": 107,
"alpha_frac": 0.5926013754,
"autogenerated": false,
"ratio": 3.699122807017544,
"config_test": false... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import tempfile
import resource
import subprocess
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def normalized_claspre_names(raw_names):
"""Convert names from claspre to "absolute" names."""
parent = None
names... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/asp/features.py",
"copies": "1",
"size": "4121",
"license": "mit",
"hash": -2385762256279919000,
"line_mean": 29.984962406,
"line_max": 98,
"alpha_frac": 0.6115020626,
"autogenerated": false,
"ratio": 3.2862838915470496,
"config_test": fa... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import uuid
import subprocess
import condor
import borg
logger = borg.get_logger(__name__, default_level = "DEBUG")
def ground_instance(asp_path, gringo_path, domain_path, ignore_errors, compat):
"""Ground an ASP instance using Gringo."""
... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/ground.py",
"copies": "1",
"size": "3329",
"license": "mit",
"hash": 6305234570006554000,
"line_mean": 29.5412844037,
"line_max": 96,
"alpha_frac": 0.5719435266,
"autogenerated": false,
"ratio": 3.5264830508474576,
"config_test": false,
"... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import os.path
import uuid
import time
import shutil
import signal
import select
import random
import tempfile
import datetime
import multiprocessing
import numpy
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
def random_seed():
"""Ret... | {
"repo_name": "borg-project/borg",
"path": "borg/solver_io.py",
"copies": "1",
"size": "7800",
"license": "mit",
"hash": -7232422010866268000,
"line_mean": 26.4647887324,
"line_max": 101,
"alpha_frac": 0.5553846154,
"autogenerated": false,
"ratio": 4.109589041095891,
"config_test": false,
"ha... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
import condor.work
if __name__ == "__main__":
plac.call(condor.work.main)
import sys
import imp
import traceback
import zmq
import condor
logger = condor.log.get_logger(__name__, default_level = "NOTSET")
def work_once(condor_id, req_socket, tas... | {
"repo_name": "borg-project/utcondor",
"path": "condor/work.py",
"copies": "1",
"size": "3413",
"license": "mit",
"hash": 712505623287191200,
"line_mean": 25.0534351145,
"line_max": 77,
"alpha_frac": 0.6120714914,
"autogenerated": false,
"ratio": 3.7505494505494505,
"config_test": false,
"has... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
import cPickle as pickle
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
@plac.annotations(
out_path = ("path to store solver"),
portfolio_name = ("name of the portfolio to train"),
solvers_path = ("path to the solve... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/train.py",
"copies": "1",
"size": "1151",
"license": "mit",
"hash": 7766794975929679000,
"line_mean": 29.2894736842,
"line_max": 96,
"alpha_frac": 0.6629018245,
"autogenerated": false,
"ratio": 3.2792022792022792,
"config_test": false,
"h... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
import sys
import logging
import cPickle as pickle
import borg
logger = borg.get_logger(__name__, default_level = "INFO")
class CompetitionFormatter(logging.Formatter):
"""A concise log formatter for output during competition."""
def __init__... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/solve.py",
"copies": "1",
"size": "2758",
"license": "mit",
"hash": 8659637850224930000,
"line_mean": 26.3069306931,
"line_max": 87,
"alpha_frac": 0.6069615664,
"autogenerated": false,
"ratio": 3.6289473684210525,
"config_test": false,
"h... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
if __name__ == "__main__":
from borg_explorer.tools.view_fit import main
plac.call(main)
import os.path
import json
import cPickle as pickle
import tarfile
import cStringIO as StringIO
import numpy
import rpy2.robjects
import rpy2.robjects.pa... | {
"repo_name": "borg-project/borg-explorer",
"path": "src/python/borg_explorer/tools/view_fit.py",
"copies": "1",
"size": "5498",
"license": "mit",
"hash": -4274125806278486500,
"line_mean": 29.5444444444,
"line_max": 116,
"alpha_frac": 0.5383775919,
"autogenerated": false,
"ratio": 3.802213001383... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
if __name__ == "__main__":
from borg_explorer.tools.view_write import main
plac.call(main)
import os.path
import csv
import json
import cPickle as pickle
import distutils.dir_util
import numpy
import jinja2
import cargo
import borg
import bor... | {
"repo_name": "borg-project/borg-explorer",
"path": "src/python/borg_explorer/tools/view_write.py",
"copies": "1",
"size": "3699",
"license": "mit",
"hash": 12901482045695892,
"line_mean": 30.6153846154,
"line_max": 89,
"alpha_frac": 0.6526088132,
"autogenerated": false,
"ratio": 3.31748878923766... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
if __name__ == "__main__":
from borg.tools.armada import main
plac.call(main)
import sys
import random
import logging
import cPickle as pickle
import numpy
import cargo
import borg
logger = cargo.get_logger(__name__, default_level = "INFO")
... | {
"repo_name": "borg-project/borg",
"path": "borg/tools/armada.py",
"copies": "1",
"size": "1148",
"license": "mit",
"hash": -3209167832984324600,
"line_mean": 25.0909090909,
"line_max": 81,
"alpha_frac": 0.631533101,
"autogenerated": false,
"ratio": 3.162534435261708,
"config_test": false,
"h... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
if __name__ == "__main__":
from cargo.tools.labor.work2 import main
plac.call(main)
import numpy
import random
import traceback
import zmq
import cargo
logger = cargo.get_logger(__name__, level = "NOTSET")
def work_once(condor_id, req_socke... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/tools/labor/work2.py",
"copies": "1",
"size": "2831",
"license": "mit",
"hash": 5725056930277920000,
"line_mean": 22.3966942149,
"line_max": 74,
"alpha_frac": 0.5990815966,
"autogenerated": false,
"ratio": 3.6718547341115433,
"config... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import plac
if __name__ == "__main__":
from cargo.tools.triggered import main
plac.call(main)
import subprocess
import pyinotify
import cargo
logger = cargo.get_logger(__name__, level = "NOTSET")
class TriggerHandler(pyinotify.ProcessEvent):
"""
... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/tools/triggered.py",
"copies": "1",
"size": "2975",
"license": "mit",
"hash": 8037923821020988000,
"line_mean": 26.8037383178,
"line_max": 101,
"alpha_frac": 0.5613445378,
"autogenerated": false,
"ratio": 4.075342465753424,
"config_t... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import random
import cPickle as pickle
import condor
from . import log
logger = condor.log.get_logger(__name__, level = "INFO")
from . import defaults
from . import raw
from . import cache
from . import managers
from . import messages
try:
import snappy
exc... | {
"repo_name": "borg-project/utcondor",
"path": "condor/__init__.py",
"copies": "1",
"size": "1962",
"license": "mit",
"hash": 7856821494570038000,
"line_mean": 24.8157894737,
"line_max": 101,
"alpha_frac": 0.6712538226,
"autogenerated": false,
"ratio": 3.908366533864542,
"config_test": false,
... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import re
import borg
logger = borg.get_logger(__name__)
def parse_sat_output(stdout):
"""Parse a solver's standard competition-format output."""
match = re.search(r"^s +(.+)$", stdout, re.M)
if match:
(answer_type,) = map(str.upper, match.g... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/sat/solvers.py",
"copies": "1",
"size": "1254",
"license": "mit",
"hash": -3326308309528993300,
"line_mean": 25.125,
"line_max": 69,
"alpha_frac": 0.5183413078,
"autogenerated": false,
"ratio": 3.710059171597633,
"config_test": false,
"... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import re
import os
import os.path
import sys
import pipes
import datetime
import cStringIO as StringIO
import subprocess
import condor
logger = condor.log.get_logger(__name__, level = "INFO")
def call_capturing(arguments, input = None, preexec_fn = None):
""... | {
"repo_name": "borg-project/utcondor",
"path": "condor/raw.py",
"copies": "1",
"size": "8568",
"license": "mit",
"hash": 8850002276834373000,
"line_mean": 25.1219512195,
"line_max": 95,
"alpha_frac": 0.5704948646,
"autogenerated": false,
"ratio": 3.9814126394052045,
"config_test": false,
"has... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import re
import os
import os.path
import sys
import time
import datetime
import subprocess
import cargo
import cStringIO as StringIO
logger = cargo.get_logger(__name__, level = "INFO")
class CondorSubmission(object):
"""Stream output to a Condor submission f... | {
"repo_name": "borg-project/cargo",
"path": "src/python/cargo/condor.py",
"copies": "1",
"size": "6173",
"license": "mit",
"hash": -2925866236527615000,
"line_mean": 25.156779661,
"line_max": 97,
"alpha_frac": 0.56423133,
"autogenerated": false,
"ratio": 3.848503740648379,
"config_test": false,... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import re
import os.path
import borg
logger = borg.get_logger(__name__)
def parse_max_sat_competition(stdout):
"""Parse output from a standard competition solver."""
optima = map(int, re.findall(r"^o +([0-9]+) *\r?$", stdout, re.M))
if len(optima) >... | {
"repo_name": "borg-project/borg",
"path": "borg/domains/max_sat/solvers.py",
"copies": "1",
"size": "2888",
"license": "mit",
"hash": 1393598621557451800,
"line_mean": 27.88,
"line_max": 85,
"alpha_frac": 0.5162742382,
"autogenerated": false,
"ratio": 3.6883780332056193,
"config_test": false,
... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import sys
import time
import random
import collections
import condor
logger = condor.log.get_logger(__name__, default_level = "INFO")
from .distributed import DistributedManager
from .parallel import ParallelManager
from .serial import SerialManager
from .http_s... | {
"repo_name": "borg-project/utcondor",
"path": "condor/managers/__init__.py",
"copies": "1",
"size": "5445",
"license": "mit",
"hash": -4141308458064184300,
"line_mean": 25.432038835,
"line_max": 77,
"alpha_frac": 0.5687786961,
"autogenerated": false,
"ratio": 4.247269890795632,
"config_test": ... |
"""@author: Bryan Silverthorn <bcs@cargo-cult.org>"""
import time
import operator
import resource
import contextlib
import borg
class Cost(object):
"""Resources."""
def __init__(self, cpu_seconds = None, wall_seconds = None):
self.cpu_seconds = None if cpu_seconds is None else float(cpu_seconds)
... | {
"repo_name": "borg-project/borg",
"path": "borg/expenses.py",
"copies": "1",
"size": "3847",
"license": "mit",
"hash": -7306897675532324000,
"line_mean": 25.7152777778,
"line_max": 114,
"alpha_frac": 0.5812321289,
"autogenerated": false,
"ratio": 3.7901477832512316,
"config_test": false,
"ha... |
__author__ = 'brycedcarter'
filename = "sampleData/data_test.dat"
packetStream = [] # this will contain the final list of packet objects
# this is the preamble and address that should be used for matching a valid packet
preambleAndAddress = [0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 1, 1, 0, 0, 1, 1, 0, 0, 0, 1, 0,... | {
"repo_name": "ProjectKarman/comm-sys-protocol-implementation",
"path": "bitStreamProcssor.py",
"copies": "1",
"size": "3471",
"license": "mit",
"hash": -1261023049258103000,
"line_mean": 43.5,
"line_max": 208,
"alpha_frac": 0.6277729761,
"autogenerated": false,
"ratio": 4.022016222479722,
"con... |
__author__ = 'bs'
# Debugging parameters
WRITE_LEFT_IMAGE = True
WRITE_LOCATION = "/home/bs/Desktop/out.jpg"
# Input video
INPUT_VIDEOS = '/home/bs/itu/graphics_and_image_analysis_SIGB/code/Stereo-Vision-System/bergar/Videos/'
VIDEO_LEFT_1 = INPUT_VIDEOS + "cameraLeft.mov"
VIDEO_... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Stereo-Vision-System/bergar/com.simonsen.stereovision/Settings/Constant.py",
"copies": "1",
"size": "1753",
"license": "apache-2.0",
"hash": -3348677096725438500,
"line_mean": 46.3783783784,
"line_max": 127,
"alpha_frac": 0.5698802054,
"... |
__author__ = 'bs'
from SIGBTools import *
from Tracker import *
import numpy as np
import cv as cv
# -----------------------------
# Global variables
# -----------------------------
files = [
"eye1.avi",
"eye2.avi",
"eye3.avi",
"eye4.avi",
"eye5.avi",
"eye6.avi",
"eye7.avi",
"eye8.avi... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Eye-Tracking-System/bergar/com.bergar.simonsen.eyetracker/Main.py",
"copies": "1",
"size": "7998",
"license": "apache-2.0",
"hash": 8514558339414949000,
"line_mean": 31.7827868852,
"line_max": 147,
"alpha_frac": 0.6029007252,
"autogenera... |
__author__ = 'bs'
import cv2
from matplotlib import *
from tools import IO
import numpy as np
from matplotlib.pyplot import *
from config.Const import *
from tools import Utils
from tools import Calc
import personMapLocation as pml
import textureMapping as tm
def texturemapObjectSequence():
""" Poor implementati... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/main.py",
"copies": "1",
"size": "2384",
"license": "apache-2.0",
"hash": -2883631979372783600,
"line_mean": 22.1553398058,
"line_max": 78,
"alpha_frac": 0.5952181208,
"autogenera... |
__author__ = 'bs'
import cv2
from matplotlib.pyplot import *
from tools import Utils
from tools import IO
from tools import Calc
from config.Const import *
from pylab import *
def showFloorTrackingData():
#Load videodata
map = cv2.imread(ITU_MAP)
fn = GROUND_FLOOR_VIDEO
cap = cv2.VideoCapture(fn)
... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/personMapLocation.py",
"copies": "1",
"size": "2759",
"license": "apache-2.0",
"hash": -7008032015193684000,
"line_mean": 28.3617021277,
"line_max": 140,
"alpha_frac": 0.5871692642,... |
__author__ = 'bs'
import cv2
from SIGBTools import *
import pylab
import numpy as np
import sys
from scipy.cluster.vq import *
from scipy.misc import imresize
from matplotlib.pyplot import *
from matplotlib import pyplot as plt
from Filter import *
def FilterPupilGlint(pupils,glints):
''' Given a list of pupil ca... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Eye-Tracking-System/bergar/com.bergar.simonsen.eyetracker/Tracker.py",
"copies": "1",
"size": "8907",
"license": "apache-2.0",
"hash": -2601893713989755000,
"line_mean": 35.805785124,
"line_max": 145,
"alpha_frac": 0.6165936904,
"autogen... |
__author__ = 'bs'
import cv2
import numpy as np
from config.Const import *
from tools import Utils
from matplotlib.pyplot import figure
def simpleTextureMap():
I1 = cv2.imread(ITU_LOGO)
I2 = cv2.imread(ITU_MAP)
#Print Help
H,Points = Utils.getHomographyFromMouse(I1,I2,4)
h, w,d = I2.shape
o... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/textureMapping.py",
"copies": "1",
"size": "2441",
"license": "apache-2.0",
"hash": 8030232565652007000,
"line_mean": 25.2580645161,
"line_max": 94,
"alpha_frac": 0.589922163,
"au... |
__author__ = 'bs'
import cv2
import numpy as np
import pylab
from pylab import *
import matplotlib as mpl
import math
from scipy import linalg
import os.path
''' This module contains sets of functions useful for basic image analysis and should be useful in the SIGB course.
Written and Assembled (2012,2013) by Dan Wi... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/tools/Utils.py",
"copies": "1",
"size": "25106",
"license": "apache-2.0",
"hash": -7882189912306916000,
"line_mean": 31.5207253886,
"line_max": 162,
"alpha_frac": 0.5548872779,
"a... |
__author__ = 'bs'
import cv2
import numpy as np
import pylab
from pylab import *
import matplotlib as mpl
import math
''' This module contains sets of functions useful for basic image analysis and should be useful in the SIGB course.
Written and Assembled (2012,2013) by Dan Witzner Hansen, IT University.
'''
def ge... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Eye-Tracking-System/bergar/com.bergar.simonsen.eyetracker/SIGBTools.py",
"copies": "1",
"size": "17102",
"license": "apache-2.0",
"hash": -3763092544119162000,
"line_mean": 33.9020408163,
"line_max": 162,
"alpha_frac": 0.5687054146,
"aut... |
__author__ = 'bs'
import numpy as np
from tools import Utils
import cv2
from tools.Utils import Camera
def calibrationExample():
camNum =0 # The number of the camera to calibrate
nPoints = 5 # number of images used for the calibration (space presses)
patternSize=(9,6) #size of the calib... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/tools/calibrationExample.py",
"copies": "1",
"size": "1340",
"license": "apache-2.0",
"hash": 7650915450502721000,
"line_mean": 34.2631578947,
"line_max": 106,
"alpha_frac": 0.62313... |
__author__ = 'bs'
import numpy as np
import cv2
def getRectangleLowerCenter(pt1, pt2):
deltax = abs(pt2[0] - pt1[0])
centerx = pt1[0] + deltax / 2
return centerx, pt2[1]
def angle_cos(p0, p1, p2):
d1, d2 = p0-p1, p2-p1
return abs( np.dot(d1, d2) / np.sqrt( np.dot(d1, d1)*np.dot(d2, d2) ) )
def ... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/tools/Calc.py",
"copies": "1",
"size": "1640",
"license": "apache-2.0",
"hash": -8069965697174358000,
"line_mean": 33.1875,
"line_max": 122,
"alpha_frac": 0.6036585366,
"autogener... |
__author__ = 'bs'
# Sequence & image files
PREFIX = "../"
BOOK = PREFIX + "BOOK/"
GRID_VIDEOS = PREFIX + "GridVideos/"
GROUND_FLOOR_DATA = PREFIX + "GroundFloorData/"
IMAGES = PREFIX + "Images/"
# Book files
BOOK_1 = BOOK + "Seq1_scene.mp4"
BOOK_2 ... | {
"repo_name": "tonybeltramelli/Graphics-And-Vision",
"path": "Projective-Geometry/bergar/com.bergar.simonsen.homography/config/Const.py",
"copies": "1",
"size": "1525",
"license": "apache-2.0",
"hash": 5504885270041008000,
"line_mean": 28.3269230769,
"line_max": 60,
"alpha_frac": 0.5436065574,
"aut... |
__author__ = 'bsoer'
from crypto.algorithms.algorithminterface import AlgorithmInterface
from tools.argparcer import ArgParcer
import tools.rsatools as RSATools
import math
import sys
class PureRSA(AlgorithmInterface):
n = None
totient = None
e = None
d = None
publicKey = None
privateKey = N... | {
"repo_name": "bensoer/pychat",
"path": "crypto/algorithms/purersa.py",
"copies": "1",
"size": "6730",
"license": "mit",
"hash": 7783865099114733000,
"line_mean": 40.0365853659,
"line_max": 116,
"alpha_frac": 0.6494799406,
"autogenerated": false,
"ratio": 3.9449003516998826,
"config_test": fals... |
__author__ = 'buckbaskin'
from graphics import *
from mathiz import locate, size, dist_sort
from math import sin, cos
from random import uniform
from visualization.NetViz import *
from leap_motion.location_sim import *
from leap_motion.simple_motion import *
def main():
win = GraphWin("My 3D view", 1920, 1080)
... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "visualization/graphClass3D.py",
"copies": "1",
"size": "2122",
"license": "mit",
"hash": -7192740826703281000,
"line_mean": 25.5375,
"line_max": 120,
"alpha_frac": 0.5815268615,
"autogenerated": false,
"ratio": 3.066473988439306,
"config_test":... |
__author__ = 'buckbaskin'
from graphics import *
from mathiz import locate, size, dist_sort
from math import sin, cos
from random import uniform
import copy
### UTILS ###
def rand_location_gen( stopper ):
return uniform(-stopper/2,stopper/2)
class ViewNode(object):
def __init__(self, x, y, z, radius, viewer)... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "visualization/NetViz.py",
"copies": "1",
"size": "2382",
"license": "mit",
"hash": -6835856739101909000,
"line_mean": 33.0428571429,
"line_max": 129,
"alpha_frac": 0.6015952981,
"autogenerated": false,
"ratio": 3.2016129032258065,
"config_test"... |
__author__ = 'buckbaskin'
from graphics import *
from mathiz import locate, size, dist_sort
from math import sin, cos
from random import uniform
def main():
win = GraphWin("My 3D view", 1920, 1080)
stopper = 8
points = [None]*50
for i in range(0,len(points),1):
points[i] = tuple([uniform(-stop... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "visualization/graph3D.py",
"copies": "1",
"size": "2287",
"license": "mit",
"hash": -7222961931492593000,
"line_mean": 30.7777777778,
"line_max": 118,
"alpha_frac": 0.6003498032,
"autogenerated": false,
"ratio": 2.905972045743329,
"config_test"... |
__author__ = 'buckbaskin'
from twitter import *
import os
class TheTwitter(object):
def __init__(self,smile_file):
self.consumer_key = smile_file.readline()[:-1]
self.consumer_secret = smile_file.readline()
if (os.path.isfile('..\\simile2.smile'))==True:
print 'use shifted fi... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "data_collection/Twitter.py",
"copies": "1",
"size": "3775",
"license": "mit",
"hash": -5018149402096177000,
"line_mean": 33.962962963,
"line_max": 138,
"alpha_frac": 0.5864900662,
"autogenerated": false,
"ratio": 3.704612365063788,
"config_test... |
__author__ = 'buckbaskin'
import os, sys, inspect, thread, time
import datetime
import math
from math import cos, sin, tan, atan2
src_dir = os.path.dirname(inspect.getfile(inspect.currentframe()))
arch_dir = '../lib/x64'
sys.path.insert(0, os.path.abspath(os.path.join(src_dir, arch_dir)))
import Leap
class LeapSimul... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "leap_motion/location_sim.py",
"copies": "1",
"size": "4719",
"license": "mit",
"hash": -4329353356843871700,
"line_mean": 34.7575757576,
"line_max": 137,
"alpha_frac": 0.5308328036,
"autogenerated": false,
"ratio": 3.1251655629139075,
"config_t... |
__author__ = 'buckbaskin'
import os, sys, inspect, thread, time
src_dir = os.path.dirname(inspect.getfile(inspect.currentframe()))
arch_dir = '../lib/x64'
sys.path.insert(0, os.path.abspath(os.path.join(src_dir, arch_dir)))
import Leap
class SampleListener(Leap.Listener):
def on_connect(self, controller):
... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "leap_motion/simple_motion.py",
"copies": "1",
"size": "1140",
"license": "mit",
"hash": -1579335045437132500,
"line_mean": 27.525,
"line_max": 96,
"alpha_frac": 0.6561403509,
"autogenerated": false,
"ratio": 3.7012987012987013,
"config_test": f... |
__author__ = 'buckbaskin'
import time
import twitter
from data_collection.Twitter import TheTwitter
from data_representation.Network import Network
def testBFS(network, length, user_id):
network.add_local_blocking(user_id, length)
def testLive(network, length):
network.add_stream_blocking(length)
if __name... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "tests/search_test.py",
"copies": "1",
"size": "1062",
"license": "mit",
"hash": -8918694805675430000,
"line_mean": 27.7027027027,
"line_max": 87,
"alpha_frac": 0.6563088512,
"autogenerated": false,
"ratio": 3.1607142857142856,
"config_test": fa... |
__author__ = 'buckbaskin'
import twitter
from data_collection.Twitter import TheTwitter
import threading
import os
class Network(object):
def __init__(self, twitter):
self.nodes = dict()# list of nodes (id , Node object)
self.connections = dict() # list of connections (id , connection weight))
... | {
"repo_name": "buckbaskin/CWRUHacks2015",
"path": "data_representation/Network.py",
"copies": "1",
"size": "6442",
"license": "mit",
"hash": 1426743462984351200,
"line_mean": 36.6783625731,
"line_max": 111,
"alpha_frac": 0.5577460416,
"autogenerated": false,
"ratio": 3.974090067859346,
"config_... |
__author__ = 'buddha'
from . import app
import models
from BeautifulSoup import BeautifulStoneSoup
from pprint import pprint
# import simplejson
import urllib, urllib2
from flask import render_template, flash, redirect, url_for
class TMError(Exception):
pass
@app.route('/')
@app.route('/index')
def index():
... | {
"repo_name": "buddha314/mizmetroweb",
"path": "app/views.py",
"copies": "1",
"size": "1160",
"license": "apache-2.0",
"hash": -4279567766473183700,
"line_mean": 23.6808510638,
"line_max": 60,
"alpha_frac": 0.6336206897,
"autogenerated": false,
"ratio": 3.411764705882353,
"config_test": false,
... |
__author__ = 'buec'
import modgrammar
import sys
from pyspeechgrammar import model
class JavaIdentifier(modgrammar.Grammar):
grammar_whitespace_mode = 'explicit'
grammar = (modgrammar.WORD("A-Za-z$", "A-Za-z0-9_$"))
def grammar_elem_init(self, session_data):
self.value = self[0].string
class ... | {
"repo_name": "ynop/pyspeechgrammar",
"path": "pyspeechgrammar/jsgf/grammars.py",
"copies": "1",
"size": "12226",
"license": "mit",
"hash": -1061808625851100900,
"line_mean": 32.3133514986,
"line_max": 121,
"alpha_frac": 0.5956976934,
"autogenerated": false,
"ratio": 3.9160794362588085,
"config... |
__author__ = 'buec'
import re
from pyspeechgrammar import parser
from pyspeechgrammar.jsgf import grammars
class JSGFParser(parser.BaseParser):
def parse_string(self, data):
p = grammars.Grammar.parser()
# remove newlines after alternative separator (performance issue)
jsgf_string = sel... | {
"repo_name": "ynop/pyspeechgrammar",
"path": "pyspeechgrammar/jsgf/__init__.py",
"copies": "1",
"size": "1193",
"license": "mit",
"hash": -192487786456882700,
"line_mean": 30.4210526316,
"line_max": 79,
"alpha_frac": 0.6253143336,
"autogenerated": false,
"ratio": 3.5191740412979353,
"config_te... |
__author__ = 'buec'
import xml.etree.ElementTree as et
from pyspeechgrammar import model
class SRGSXMLSerializer:
def create_grammar_element(self, grammar):
grammar_element = et.Element('grammar')
for rule in grammar.rules:
rule_element = self.create_rule_element(rule)
... | {
"repo_name": "ynop/pyspeechgrammar",
"path": "pyspeechgrammar/srgs_xml/serialize.py",
"copies": "1",
"size": "4614",
"license": "mit",
"hash": -5587511030674739000,
"line_mean": 39.1304347826,
"line_max": 105,
"alpha_frac": 0.6497615951,
"autogenerated": false,
"ratio": 3.7000801924619084,
"co... |
__author__ = 'buec'
class Grammar:
def __init__(self, name="", language="en-US", encoding=""):
self.name = name
self.language = language
self.encoding = encoding
self.rules = []
self.root_rule = None
def add_rule(self, rule):
if self.contains_rule_with_name(rul... | {
"repo_name": "ynop/pyspeechgrammar",
"path": "pyspeechgrammar/model.py",
"copies": "1",
"size": "4095",
"license": "mit",
"hash": 7675188225440731000,
"line_mean": 30.0227272727,
"line_max": 140,
"alpha_frac": 0.6161172161,
"autogenerated": false,
"ratio": 3.6271036315323295,
"config_test": fa... |
__author__ = 'bug85'
#!python
# coding=utf-8
import os, sys, subprocess, hashlib, re, tempfile, binascii, base64
import rsa, requests
import tea
def fromhex(s):
# Python 3: bytes.fromhex
return bytes(bytearray.fromhex(s))
pubKey=rsa.PublicKey(int(
'F20CE00BAE5361F8FA3AE9CEFA495362'
'FF7DA1BA628F64A347F0A8C012BF0... | {
"repo_name": "azber/QQLib-python",
"path": "qq_lib.py",
"copies": "1",
"size": "2572",
"license": "apache-2.0",
"hash": 4017790604384419000,
"line_mean": 31.5696202532,
"line_max": 102,
"alpha_frac": 0.5754276827,
"autogenerated": false,
"ratio": 2.454198473282443,
"config_test": false,
"has... |
__author__ = 'bukun@osgeo.cn'
import tornado.web
from pycate.model.catalog_model import MCatalog
from pycate.module import imgslide_module
from pycate.module import refreshinfo_module
from pycate.module import showjianli_module
ImgSlide = imgslide_module.ImgSlide
RefreshInfo = refreshinfo_module.RefreshInfo
ShowJian... | {
"repo_name": "jiaxiaolei/pycate",
"path": "core/modules.py",
"copies": "1",
"size": "2642",
"license": "mit",
"hash": 33791322681051800,
"line_mean": 28.4719101124,
"line_max": 90,
"alpha_frac": 0.6003051106,
"autogenerated": false,
"ratio": 3.229064039408867,
"config_test": false,
"has_no_k... |
__author__ = 'bukun'
# __all__ = ['get_uid', 'md5','get_timestamp', 'get_time_str', 'markit']
import uuid
import hashlib
import time
def get_uid():
return( str(uuid.uuid1()))
def md5(instr):
# if type(instr) is bytes:
m = hashlib.md5()
m.update(instr.encode('utf-8'))
return m.hexdig... | {
"repo_name": "jiaxiaolei/pycate",
"path": "libs/tool.py",
"copies": "1",
"size": "2096",
"license": "mit",
"hash": -8400251608777745000,
"line_mean": 31.5806451613,
"line_max": 118,
"alpha_frac": 0.5067307692,
"autogenerated": false,
"ratio": 3.2,
"config_test": false,
"has_no_keywords": fal... |
__author__ = 'bukun'
from torlite.model.mpost import MPost
def get_dic():
out_arr = []
with open('./keywords_dic.txt') as fi:
uu = fi.readlines()
for u in uu:
u = u.strip()
if len(u) > 0:
tt = u.split()
out_arr.append(tt)
... | {
"repo_name": "Geoion/TorCMS",
"path": "update_keywords.py",
"copies": "3",
"size": "1112",
"license": "mit",
"hash": -6203769971211191000,
"line_mean": 22.6818181818,
"line_max": 76,
"alpha_frac": 0.4659300184,
"autogenerated": false,
"ratio": 2.865435356200528,
"config_test": false,
"has_no... |
__author__ = 'bukun'
import pickle
class cNode(object):
def __init__(self):
self.children = None
# The encode of word is UTF-8
# The encode of message is UTF-8
class cDfa(object):
def __init__(self):
# self.pklfile = 'sdaf.pkl'
self.root=cNode()
# The encode of w... | {
"repo_name": "jiaxiaolei/pycate",
"path": "libs/dfa.py",
"copies": "1",
"size": "2827",
"license": "mit",
"hash": 7184112677823788000,
"line_mean": 26.4545454545,
"line_max": 74,
"alpha_frac": 0.4437344693,
"autogenerated": false,
"ratio": 3.6727509778357237,
"config_test": false,
"has_no_ke... |
__author__ = 'buyvich'
from pprint import pprint
import logging
import functools
import json
import sqlalchemy
from tornado.web import RequestHandler
from weekly_training.settings import TemplateEngine, get_session
from weekly_training.models import *
LOG = logging.getLogger()
def auth(f):
@functools.wraps(... | {
"repo_name": "gh0st-dog/weekly-tng",
"path": "weekly_training/handlers.py",
"copies": "1",
"size": "2774",
"license": "mit",
"hash": -2235702650978240300,
"line_mean": 24.6944444444,
"line_max": 73,
"alpha_frac": 0.5937274694,
"autogenerated": false,
"ratio": 4.002886002886003,
"config_test": ... |
__author__ = 'bwagner'
import os
from ConfigParser import SafeConfigParser
from ScriptLog import log, log2, closeLog, error, warning, info, debug, entry, exit, lopen, handleException
try:
config = SafeConfigParser()
config.read( './Configuration/base.cfg' )
logFileDir = str( config.get( "base", "logDir" ) ... | {
"repo_name": "wags007/BIND_DHCP_to_dnsmasq",
"path": "dhcpTodnsmasq.py",
"copies": "1",
"size": "4491",
"license": "apache-2.0",
"hash": 3655429254421607000,
"line_mean": 47.8152173913,
"line_max": 112,
"alpha_frac": 0.5315074594,
"autogenerated": false,
"ratio": 4.411591355599215,
"config_tes... |
__author__ = 'bwagner'
import sys, argparse
import json
#from fabric.api import *
try:
import requests
from requests.auth import HTTPBasicAuth
# if int(requests.__version__.split('.')[1]) < 12:
# print "You may have to upgrade your python requests version!"
except:
print "Please install pytho... | {
"repo_name": "wags007/podcastMaster",
"path": "AuphonicsProcessing/processHangout.py",
"copies": "1",
"size": "2423",
"license": "apache-2.0",
"hash": -3741447997403820000,
"line_mean": 36.859375,
"line_max": 126,
"alpha_frac": 0.6108130417,
"autogenerated": false,
"ratio": 4.058626465661642,
... |
__author__ = 'bwall'
import json
import os
import time
import ExtractHosts
import base64
def create_command_to_run(pbot, output_folder=None, proxy=None):
if proxy is None:
proxy = ""
else:
proxy = "--proxy {0}".format(proxy)
password = ""
if 'pass' in pbot['information']:
password = "-p '{0}'".format(pbot[... | {
"repo_name": "bwall/ircsnapshot",
"path": "ircsnapshot/run_from_json.py",
"copies": "1",
"size": "1196",
"license": "mit",
"hash": -7179033803488863000,
"line_mean": 22.4705882353,
"line_max": 104,
"alpha_frac": 0.6588628763,
"autogenerated": false,
"ratio": 2.9029126213592233,
"config_test": ... |
__author__ = 'bwall'
import markovobfuscate.obfuscation as obf
import logging
import re
import random
if __name__ == "__main__":
logging.basicConfig(level=logging.DEBUG)
# Regular expression to split our training files on
split_regex = r'\n'
# File/book to read for training the Markov model (will be ... | {
"repo_name": "bwall/markovobfuscate",
"path": "testing.py",
"copies": "1",
"size": "1182",
"license": "mit",
"hash": 2767963912356903400,
"line_mean": 30.972972973,
"line_max": 105,
"alpha_frac": 0.641285956,
"autogenerated": false,
"ratio": 3.592705167173252,
"config_test": false,
"has_no_k... |
__author__ = 'byt3smith'
#
# Generates a dir for carbonblack feeds
# Can also stand up a SimpleHTTPServer to host the feeds
#
#stdlib
from os import chdir, listdir, mkdir, getcwd, path
import http.server
import socketserver
from re import sub, search
from json import dump, loads
from socket import gethostname
#pypi
fr... | {
"repo_name": "byt3smith/Forager",
"path": "forager/cb_tools.py",
"copies": "1",
"size": "7841",
"license": "mit",
"hash": -5407950016755085000,
"line_mean": 29.62890625,
"line_max": 128,
"alpha_frac": 0.544700931,
"autogenerated": false,
"ratio": 3.6217090069284064,
"config_test": false,
"ha... |
__author__ = 'byt3smith'
#
# Generates a dir for carbonblack feeds
# Can also stand up a SimpleHTTPServer to host the feeds
#
#stdlib
from os import chdir, listdir, mkdir, getcwd, path
import SimpleHTTPServer
import SocketServer
from re import sub, search
from json import dump, loads
from socket import gethostname
#py... | {
"repo_name": "sberrydavis/Forager",
"path": "bin/cb_tools.py",
"copies": "1",
"size": "7370",
"license": "mit",
"hash": -5996535434025156000,
"line_mean": 29.2049180328,
"line_max": 128,
"alpha_frac": 0.5598371777,
"autogenerated": false,
"ratio": 3.6074400391581007,
"config_test": false,
"h... |
__author__ = 'byt3smith'
#
# Purpose: Import module for pulling and formatting
# all necessary intelligence feeds
#
from tools import *
from re import search
ip_addr = regex('ip')
hostname = regex('domain')
class FeedModules():
## Malc0de
def malc0de_update(self):
iocs = gather('http://malc0... | {
"repo_name": "sberrydavis/Forager",
"path": "bin/feeds.py",
"copies": "1",
"size": "5061",
"license": "mit",
"hash": -8740538772429036000,
"line_mean": 31.6516129032,
"line_max": 110,
"alpha_frac": 0.6342620036,
"autogenerated": false,
"ratio": 3.050632911392405,
"config_test": false,
"has_n... |
__author__ = 'byt3smith'
#
# Purpose: Import module for pulling and formatting
# all necessary intelligence feeds
#
from .tools import *
from re import search
ip_addr = regex('ip')
hostname = regex('domain')
class FeedModules():
## Malc0de
def malc0de_update(self):
iocs = gather('http://malc... | {
"repo_name": "byt3smith/Forager",
"path": "forager/feeds.py",
"copies": "1",
"size": "5266",
"license": "mit",
"hash": -2307119241185062000,
"line_mean": 31.5061728395,
"line_max": 110,
"alpha_frac": 0.6355867831,
"autogenerated": false,
"ratio": 3.0474537037037037,
"config_test": false,
"ha... |
__author__ = 'byt3smith'
#
# Purpose: Tools for gathering IP addresses, domain names, URL's, etc..
#
from time import sleep
from os import chdir, path
from xlrd import open_workbook, sheet
import re
import sys
import urllib2
import pdfConverter
import unicodedata
from colorama import Fore, Back, Style, init
init(auto... | {
"repo_name": "sberrydavis/Forager",
"path": "bin/tools.py",
"copies": "1",
"size": "7402",
"license": "mit",
"hash": -6466015792744461000,
"line_mean": 28.967611336,
"line_max": 206,
"alpha_frac": 0.4970278303,
"autogenerated": false,
"ratio": 3.03734099302421,
"config_test": false,
"has_no_... |
__author__ = 'byt3smith'
#
# When called, will search through Intel directory for each
# indicator in provided CSV or New-line formatted file.
#
from . import tools
import sys
import re
import os
from time import sleep
def search_file(ioc):
os.chdir('../')
patt = tools.regex('ip')
if ioc[-3:] == 'csv':
... | {
"repo_name": "byt3smith/Forager",
"path": "forager/hunt.py",
"copies": "1",
"size": "2380",
"license": "mit",
"hash": -1021396705119809000,
"line_mean": 23.7916666667,
"line_max": 96,
"alpha_frac": 0.5163865546,
"autogenerated": false,
"ratio": 3.689922480620155,
"config_test": false,
"has_n... |
__author__ = 'byt3smith'
#
# When called, will search through Intel directory for each
# indicator in provided CSV or New-line formatted file.
#
import tools
import sys
import re
import os
from time import sleep
def search_file(ioc):
os.chdir('../')
patt = tools.regex('ip')
if ioc[-3:] == 'csv':
... | {
"repo_name": "sberrydavis/Forager",
"path": "bin/hunt.py",
"copies": "1",
"size": "2380",
"license": "mit",
"hash": -6957330248932108000,
"line_mean": 23.0404040404,
"line_max": 96,
"alpha_frac": 0.5168067227,
"autogenerated": false,
"ratio": 3.707165109034268,
"config_test": false,
"has_no_... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.