text stringlengths 0 1.05M | meta dict |
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from ...externals.six import string_types
import os
import numpy as np
from numpy.testing import assert_allclose
from nose.tools import (assert_equal, assert_almost_equal, assert_false,
assert_raises, assert_true)
import warnings
import mne
from mne.datasets import sample
from mne.io.kit.test... | {
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"path": "mne/gui/tests/test_coreg_gui.py",
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import os
import os.path as op
import re
import shutil
import sys
from unittest import SkipTest
import numpy as np
from numpy.testing import (assert_allclose, assert_equal,
assert_array_almost_equal)
import pytest
import mne
from mne.datasets import testing
from mne.io.kit.tests import dat... | {
"repo_name": "adykstra/mne-python",
"path": "mne/gui/tests/test_coreg_gui.py",
"copies": "1",
"size": "11263",
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"confi... |
import os
import os.path as op
import re
import shutil
import numpy as np
from numpy.testing import assert_allclose, assert_array_almost_equal
import pytest
import mne
from mne.datasets import testing
from mne.io.kit.tests import data_dir as kit_data_dir
from mne.surface import dig_mri_distances
from mne.transforms ... | {
"repo_name": "pravsripad/mne-python",
"path": "mne/gui/tests/test_coreg_gui.py",
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... |
import os
from numpy import array
from numpy.testing import assert_allclose
from nose.tools import assert_equal, assert_false, assert_raises, assert_true
from mne.datasets import testing
from mne.io.tests import data_dir as fiff_data_dir
from mne.utils import (_TempDir, requires_mne, requires_freesurfer,
... | {
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"path": "mne/gui/tests/test_file_traits.py",
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__author__ = 'christianbuia'
import binascii
def fixed_xor_hexstrings(hexstring1, key):
import binascii
bytes1=binascii.unhexlify(hexstring1)
decoded = ""
for byte in bytes1:
decoded+=chr(byte^key)
return decoded
#----------------------------------------------------------------------------... | {
"repo_name": "8u1a/my_matasano_crypto_challenges",
"path": "set1/challenge4.py",
"copies": "1",
"size": "21733",
"license": "unlicense",
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__author__ = 'christianbuia'
from Crypto.Cipher import AES
import base64
#-----------------------------------------------------------------------------------------------------------------------
def solve_challenge(b64_crypt):
ciphertext = base64.decodebytes(bytes(b64_crypt, "ascii"))
key="YELLOW SUBMARINE"
... | {
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"path": "set1/challenge7.py",
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__author__ = 'christianbuia'
from Crypto.Cipher import AES
import base64
#-----------------------------------------------------------------------------------------------------------------------
def pkcs7_padding(message_bytes, block_size):
#message_bytes=bytearray(message_bytes)
pad_length = block_size - ... | {
"repo_name": "8u1a/my_matasano_crypto_challenges",
"path": "set2/challenge10.py",
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__author__ = 'christianbuia'
import binascii
import sys
import base64
def hamming_distance_two_hexstrings(hexstring1, hexstring2):
distance = 0
if len(hexstring1) != len(hexstring2):
sys.stderr.write("unexpected: length of compared strings don't match. exiting.\n")
return False
bytes1 =... | {
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"path": "set1/challenge6.py",
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"size": "9251",
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"co... |
__author__ = 'christianbuia'
import random
from Crypto.Cipher import AES
import base64
import sys
def pkcs7_padding(message_bytes, block_size):
pad_length = block_size - (len(message_bytes) % block_size)
if pad_length != block_size:
for i in range(0, pad_length):
message_bytes += bytes([p... | {
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"path": "set2/challenge14.py",
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"c... |
__author__ = 'christianbuia'
import random
from Crypto.Cipher import AES
import base64
def pkcs7_padding(message_bytes, block_size):
pad_length = block_size - (len(message_bytes) % block_size)
if pad_length != block_size:
for i in range(0, pad_length):
message_bytes += bytes([pad_length]... | {
"repo_name": "8u1a/my_matasano_crypto_challenges",
"path": "set2/challenge12.py",
"copies": "1",
"size": "6174",
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"autogenerated": false,
"ratio": 4.059171597633136,
"co... |
__author__ = 'christianbuia'
import random
from Crypto.Cipher import AES
def add_pkcs7_padding(message_bytes, blocksize):
pad_length = blocksize - (len(message_bytes) % blocksize)
for i in range(0, pad_length):
message_bytes += bytes([pad_length])
return message_bytes
#------------------------... | {
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"path": "set2/challenge16.py",
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__author__ = 'christianbuia'
import random
from Crypto.Cipher import AES
def pkcs7_padding(message_bytes, block_size):
#message_bytes=bytearray(message_bytes)
pad_length = block_size - (len(message_bytes) % block_size)
if pad_length != block_size:
for i in range(0, pad_length):
#mes... | {
"repo_name": "8u1a/my_matasano_crypto_challenges",
"path": "set2/challenge11.py",
"copies": "1",
"size": "5574",
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"autogenerated": false,
"ratio": 4.557645134914146,
"con... |
__author__ = 'christianbuia'
import random
from Crypto.Cipher import AES
def pkcs7_padding(message_bytes, block_size):
pad_length = block_size - (len(message_bytes) % block_size)
if pad_length != block_size:
for i in range(0, pad_length):
message_bytes += bytes([pad_length])
return... | {
"repo_name": "8u1a/my_matasano_crypto_challenges",
"path": "set2/challenge13.py",
"copies": "1",
"size": "3792",
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"... |
import numpy
from heapq import *
def heuristic(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
def astar(array, start, goal):
#diagonal movement allowed
neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]
#without diagonal movement
#neighbors = [(0,1),(0,-1),(1,0),(-1,0)]
... | {
"repo_name": "OPU-Surveillance-System/monitoring",
"path": "master/scripts/planner/astar.py",
"copies": "1",
"size": "2025",
"license": "mit",
"hash": 5498926348619165000,
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"line_max": 100,
"alpha_frac": 0.5180246914,
"autogenerated": false,
"ratio": 3.5526315789473686,... |
import numpy
from heapq import *
def heuristic(a, b):
return (b[0] - a[0]) ** 2 + (b[1] - a[1]) ** 2
def astar(array, start, goal):
neighbors = [(0,1),(0,-1),(1,0),(-1,0),(1,1),(1,-1),(-1,1),(-1,-1)]
close_set = set()
came_from = {}
gscore = {start:0}
fscore = {start:heuristic(start, goal)... | {
"repo_name": "awwong1/2016-pason-coding-contest",
"path": "test_astar.py",
"copies": "2",
"size": "2483",
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"config_test": f... |
__author__ = 'Christian Christelis <christian@kartoza.com>'
__date__ = '06/09/16'
__license__ = "GPL"
__copyright__ = 'kartoza.com'
# coding=utf-8
"""Model class for Occupations"""
from django.contrib.gis.db import models
from feti.models.learning_pathway import LearningPathway
class Occupation(models.Model):
"... | {
"repo_name": "cchristelis/feti",
"path": "django_project/feti/models/occupation.py",
"copies": "1",
"size": "1032",
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"autogenerated": false,
"ratio": 3.4285714285714284,... |
__author__ = 'Christian Christelis <christian@kartoza.com>'
__date__ = '10/04/16'
import json
from django.contrib import messages
from django.http import Http404, HttpResponse
from django.views.generic.edit import FormView
from healthsites.forms.assessment_form import AssessmentForm
from healthsites.utils import heal... | {
"repo_name": "cchristelis/watchkeeper",
"path": "django_project/healthsites/views/healthsites_view.py",
"copies": "1",
"size": "2518",
"license": "bsd-2-clause",
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"line_mean": 34.9714285714,
"line_max": 89,
"alpha_frac": 0.6592533757,
"autogenerated": false,
"ratio": ... |
__author__ = 'Christian Christelis <christian@kartoza.com>'
__date__ = '10/04/16'
import uuid
from django import forms
from django.contrib.gis.geos import Point
from healthsites.models.healthsite import Healthsite
from healthsites.tasks.regenerate_cache import regenerate_cache
from healthsites.models.assessment import... | {
"repo_name": "cchristelis/watchkeeper",
"path": "django_project/healthsites/forms/assessment_form.py",
"copies": "1",
"size": "5365",
"license": "bsd-2-clause",
"hash": 1663090584340770800,
"line_mean": 41.5793650794,
"line_max": 93,
"alpha_frac": 0.6096924511,
"autogenerated": false,
"ratio": 4... |
__author__ = 'Christian Christelis <christian@kartoza.com>'
__date__ = '15/04/16'
import requests
from django.core.exceptions import ObjectDoesNotExist
from django.core.management.base import BaseCommand
from django.contrib.gis.geos import Point
from healthsites.models.healthsite import Healthsite
import logging
l... | {
"repo_name": "cchristelis/watchkeeper",
"path": "django_project/healthsites/management/commands/harvest_healthsites.py",
"copies": "1",
"size": "1983",
"license": "bsd-2-clause",
"hash": 4282295619999742500,
"line_mean": 32.05,
"line_max": 74,
"alpha_frac": 0.6011094302,
"autogenerated": false,
... |
__author__ = 'Christian Christelis <christian@kartoza.com>'
__date__ = '21/04/16'
from django.contrib.gis.db import models
from django.contrib.contenttypes.fields import GenericForeignKey
from django.contrib.contenttypes.models import ContentType
from healthsites.models.healthsite import Healthsite
RESULTOPTIONS = ... | {
"repo_name": "cchristelis/watchkeeper",
"path": "django_project/healthsites/models/assessment.py",
"copies": "1",
"size": "2924",
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"line_max": 76,
"alpha_frac": 0.6809165527,
"autogenerated": false,
"ratio": 3.956... |
__author__ = 'Christian Kater'
from redmine import Redmine
import random, string, re, csv, sys
config = {}
execfile("redmine.conf", config)
redmine = Redmine(config['redmine_host'], key=config['redmine_rest_key'])
def get_key(login, parent=None):
key = re.sub('[^a-z0-9]+', '', login.lower())
if parent:
... | {
"repo_name": "ChKater/redmine-student-administration",
"path": "redmine_student_administration.py",
"copies": "1",
"size": "3261",
"license": "apache-2.0",
"hash": -6794741228925122000,
"line_mean": 32.2857142857,
"line_max": 106,
"alpha_frac": 0.6360012266,
"autogenerated": false,
"ratio": 3.55... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
import numpy as np
import typing
def algae(relative_humidity: typing.List[float], temperature: typing.List[float], material_name, porosity, roughness,
total_pore_area):
"""
UNIVPM Algae Model
Currently a dummy function!
:param relativ... | {
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"path": "data_process/algae_script/algae_model.py",
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"autogenerated": false,
"ratio": 3.00602983179942... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
import pandas as pd
from delphin_6_automation.database_interactions import mongo_setup
from delphin_6_automation.database_interacti... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/sample_check/raw_samples.py",
"copies": "1",
"size": "1132",
"license": "mit",
"hash": 692229549516555000,
"line_mean": 34.375,
"line_max": 120,
"alpha_frac": 0.5697879859,
"autogenerated": false,
"ratio": 4.042857142857143,
"con... |
__author__ = "Christian Kongsgaard"
__license__ = "MIT"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
from datetime import datetime
# Modules:
import mongoengine
# RiBuild Modules:
import delphin_6_automation.database_interactions.... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/db_templates/result_raw_entry.py",
"copies": "1",
"size": "1153",
"license": "mit",
"hash": -5855873261787005000,
"line_mean": 37.4333333333,
"line_max": 120,
"alpha_frac": 0.6062445794,
"autogenerated":... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import copy
import scipy.interpolate as ip
import numpy as np
import datetime
# RiBuild Modules
from delphin_6_automatio... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/delphin_setup/material_modelling.py",
"copies": "1",
"size": "7790",
"license": "mit",
"hash": 4314642199278446000,
"line_mean": 59.859375,
"line_max": 120,
"alpha_frac": 0.5163029525,
"autogenerated": false,
"ratio": 3.980... |
__author__ = 'Christian Kongsgaard'
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import logging
import os
# RiBuild Modules:
# ------------------------------------------------------------------------... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/logging/ribuild_logger.py",
"copies": "1",
"size": "1475",
"license": "mit",
"hash": 5566476617321507000,
"line_mean": 26.8301886792,
"line_max": 120,
"alpha_frac": 0.5003389831,
"autogenerated": false,
"ratio": 4.469696969... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import lxml.etree as et
import xmltodict
import datetime
import os
import shutil
import bson
import typing
import numpy ... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/file_parsing/delphin_parser.py",
"copies": "1",
"size": "21944",
"license": "mit",
"hash": -4771985528817925000,
"line_mean": 34.5080906149,
"line_max": 120,
"alpha_frac": 0.5464363835,
"autogenerated": false,
"ratio": 3.52... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import matplotlib.pyplot as plt
import numpy as np
import os
import datetime
import matplotlib.dates as mdates
import pan... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/2d_1d/archieve/relative_humidity.py",
"copies": "1",
"size": "18433",
"license": "mit",
"hash": 3284509745293186000,
"line_mean": 44.5135802469,
"line_max": 120,
"alpha_frac": 0.5276406445,
"autogenerated": false,
"ratio": 3.076781... |
__author__ = "Christian Kongsgaard"
__license__ = "MIT"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import mongoengine
from datetime import datetime
# RiBuild Modules:
import delphin_6_automation.database_interactions.d... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/db_templates/delphin_entry.py",
"copies": "1",
"size": "2678",
"license": "mit",
"hash": 2654755857897441000,
"line_mean": 40.2,
"line_max": 120,
"alpha_frac": 0.7038834951,
"autogenerated": false,
"ra... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import mongoengine
import delphin_6_automation.database_interactions.database_collections as collections
# RiBuild Modu... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/db_templates/normalized_entry.py",
"copies": "1",
"size": "1415",
"license": "mit",
"hash": 781766919374139300,
"line_mean": 33.512195122,
"line_max": 120,
"alpha_frac": 0.6070671378,
"autogenerated": fa... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import numpy as np
from typing import Tuple
import bson
# RiBuild Modules
from delphin_6_automation.logging.ribuild_lo... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/normalize_data/algea.py",
"copies": "1",
"size": "3056",
"license": "mit",
"hash": 9178973254214587000,
"line_mean": 33.7272727273,
"line_max": 120,
"alpha_frac": 0.6397251309,
"autogenerated": false,
"ratio": 3.4687854710556185,
... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import os
from collections import OrderedDict
import typing
# RiBuild Modules:
import delphin_6_automation.database_int... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/material_interactions.py",
"copies": "1",
"size": "5860",
"license": "mit",
"hash": -7142672257858925000,
"line_mean": 35.1728395062,
"line_max": 120,
"alpha_frac": 0.6104095563,
"autogenerated": false,
... |
__author__ = "Christian Kongsgaard"
__license__ = "MIT"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import os
import datetime
import time
import shutil
import typing
from mongoengine import Q
# RiBuild Modules:
from del... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/simulation_interactions.py",
"copies": "1",
"size": "9558",
"license": "mit",
"hash": -5774087173877538000,
"line_mean": 30.3409836066,
"line_max": 120,
"alpha_frac": 0.6383134547,
"autogenerated": false... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import os
import json
import pandas as pd
import datetime
import bson
# RiBuild Modules
from delphin_6_automation.databa... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/validation/upload_data_to_db.py",
"copies": "1",
"size": "10200",
"license": "mit",
"hash": 8452626281916862000,
"line_mean": 39.6374501992,
"line_max": 120,
"alpha_frac": 0.5288235294,
"autogenerated": false,
"ratio": 4.1145623235... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import os
import numpy as np
import bson
# RiBuild Modules:
from delphin_6_automation.database_interactions.db_template... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/weather_interactions.py",
"copies": "1",
"size": "16063",
"license": "mit",
"hash": 1107403312416815200,
"line_mean": 44.1207865169,
"line_max": 129,
"alpha_frac": 0.5886198095,
"autogenerated": false,
... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import os
import numpy as np
import matplotlib.pyplot as plt
import pandas
# RiBuild Modules
from delphin_6_automation.f... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/failed_simulations/process_failed.py",
"copies": "1",
"size": "1888",
"license": "mit",
"hash": -4192356046566729700,
"line_mean": 28.0461538462,
"line_max": 120,
"alpha_frac": 0.6038135593,
"autogenerated": false,
"ratio": 2.90909... |
__author__ = "Christian Kongsgaard"
__license__ = "MIT"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import os
import yaml
import numpy as np
# RiBuild Modules:
from delphin_6_automation.database_interactions.db_template... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/general_interactions.py",
"copies": "1",
"size": "8724",
"license": "mit",
"hash": 6288229648234507000,
"line_mean": 32.5538461538,
"line_max": 120,
"alpha_frac": 0.659215956,
"autogenerated": false,
"... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import os
# RiBuild Modules
from delphin_6_automation.database_interactions.db_templates import delphin_entry
from delph... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/wp6_v2/sim_status.py",
"copies": "1",
"size": "2116",
"license": "mit",
"hash": -8618433809592759000,
"line_mean": 46.0222222222,
"line_max": 120,
"alpha_frac": 0.6252362949,
"autogenerated": false,
"ratio": 3.8194945848375452,
"... |
__author__ = "Christian Kongsgaard"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import getpass
import os
import pathlib
import pandas as pd
# RiBuild Modules
from delphin_6_automation.database_intera... | {
"repo_name": "thp44/delphin_6_automation",
"path": "data_process/2d_1d/simon/download.py",
"copies": "1",
"size": "2206",
"license": "mit",
"hash": -6558662757134022000,
"line_mean": 33.46875,
"line_max": 128,
"alpha_frac": 0.6591115141,
"autogenerated": false,
"ratio": 3.409582689335394,
"con... |
__author__ = "Christian Kongsgaard"
__license__ = "MIT"
__version__ = "0.0.1"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
from datetime import datetime
# Modules:
import mongoengine
# RiBuild Modules:
import delphin_6_automation.... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/database_interactions/db_templates/weather_entry.py",
"copies": "1",
"size": "1426",
"license": "mit",
"hash": 2360547615695469000,
"line_mean": 36.5263157895,
"line_max": 120,
"alpha_frac": 0.5778401122,
"autogenerated": fal... |
__author__ = "Christian Kongsgaard"
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import datetime
import os
# RiBuild Modules:
# ------------------------------------------------------------------------------------------... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/file_parsing/weather_parser.py",
"copies": "1",
"size": "10581",
"license": "mit",
"hash": -3527160306274728000,
"line_mean": 40.33203125,
"line_max": 147,
"alpha_frac": 0.4619601172,
"autogenerated": false,
"ratio": 4.0370... |
__author__ = "Christian Kongsgaard, Simon Jørgensen"
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules
import pandas as pd
import numpy as np
import xmltodict
import os
from collections import defaultdict
im... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/sampling/inputs.py",
"copies": "1",
"size": "13226",
"license": "mit",
"hash": -2398966878389577700,
"line_mean": 36.5710227273,
"line_max": 120,
"alpha_frac": 0.5615122873,
"autogenerated": false,
"ratio": 4.00030248033877... |
__author__ = 'Christian Kongsgaard, Thomas Perkov'
__license__ = 'MIT'
# -------------------------------------------------------------------------------------------------------------------- #
# IMPORTS
# Modules:
import os
import platform
from pathlib import Path
import subprocess
import datetime
import time
import t... | {
"repo_name": "thp44/delphin_6_automation",
"path": "delphin_6_automation/backend/simulation_worker.py",
"copies": "1",
"size": "22905",
"license": "mit",
"hash": -8713531379235280000,
"line_mean": 34.6775700935,
"line_max": 121,
"alpha_frac": 0.5893036455,
"autogenerated": false,
"ratio": 3.8016... |
__author__ = 'Christian'
from DbRequests import DbRequests
from scipy.stats import pearsonr
import numpy as np
import itertools
class RecommenderSystem:
def __init__(self):
self.db = DbRequests()
self.blacklist = ['A TripAdvisor Member', 'lass=', 'Posted by a La Quinta traveler', 'Posted by an Ea... | {
"repo_name": "LukasGentele/Graph-based-Hotel-Recommendations",
"path": "rs/RecommenderSystem.py",
"copies": "1",
"size": "17214",
"license": "apache-2.0",
"hash": 8997820555788160000,
"line_mean": 34.1306122449,
"line_max": 697,
"alpha_frac": 0.5314279075,
"autogenerated": false,
"ratio": 3.5617... |
__author__ = 'christian'
import csv
import logging
from feti.models.campus import Campus as Provider
from feti.models.provider import Provider as PrimaryInstitute
# logging.basicConfig(filename='provider.log')
header = []
original_providers = []
new_providers = []
primary_institutes = []
with open('feti_campus.csv',... | {
"repo_name": "cchristelis/feti",
"path": "deployment/setup_data/data_cleanup/refactor_providers.py",
"copies": "2",
"size": "2833",
"license": "bsd-2-clause",
"hash": 6131682844907570000,
"line_mean": 33.5487804878,
"line_max": 79,
"alpha_frac": 0.6399576421,
"autogenerated": false,
"ratio": 3.9... |
__author__ = 'christian'
import csv
import logging
from feti.models.provider import Provider as PrimaryInstitute
logging.basicConfig(filename='primary_institute.log')
header = []
original_primary_institutes = []
new_primary_institutes = []
with open('primary_institution_duplicates.csv', 'r') as csv_file:
csv_rea... | {
"repo_name": "cchristelis/feti",
"path": "deployment/setup_data/data_cleanup/refactor_primary_institutes.py",
"copies": "2",
"size": "2332",
"license": "bsd-2-clause",
"hash": 4491478617309552000,
"line_mean": 34.8769230769,
"line_max": 89,
"alpha_frac": 0.6509433962,
"autogenerated": false,
"ra... |
import numpy as np
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_almost_equal
from sklearn.decomposition import FactorAnalysis
def test_factor_analysis():
"""Test FactorAnalysis ability to recover the data covariance struc... | {
"repo_name": "seckcoder/lang-learn",
"path": "python/sklearn/sklearn/decomposition/tests/test_factor_analysis.py",
"copies": "2",
"size": "1692",
"license": "unlicense",
"hash": -5542598162930436000,
"line_mean": 30.9245283019,
"line_max": 75,
"alpha_frac": 0.6678486998,
"autogenerated": false,
... |
import numpy as np
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_almost_equal
from skl... | {
"repo_name": "xyguo/scikit-learn",
"path": "sklearn/decomposition/tests/test_factor_analysis.py",
"copies": "16",
"size": "3203",
"license": "bsd-3-clause",
"hash": 3558450733467062000,
"line_mean": 36.6823529412,
"line_max": 76,
"alpha_frac": 0.6625039026,
"autogenerated": false,
"ratio": 3.500... |
import warnings
import numpy as np
from sklearn.utils.testing import assert_true
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_almost... | {
"repo_name": "B3AU/waveTree",
"path": "sklearn/decomposition/tests/test_factor_analysis.py",
"copies": "4",
"size": "3346",
"license": "bsd-3-clause",
"hash": -1118620781258024700,
"line_mean": 36.1777777778,
"line_max": 81,
"alpha_frac": 0.6589958159,
"autogenerated": false,
"ratio": 3.51840168... |
import numpy as np
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_equal
from sklearn.utils.testing import assert_greater
from sklearn.utils.testing import assert_less
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_almost_equal
from skl... | {
"repo_name": "sonnyhu/scikit-learn",
"path": "sklearn/decomposition/tests/test_factor_analysis.py",
"copies": "112",
"size": "3203",
"license": "bsd-3-clause",
"hash": -649296008559016400,
"line_mean": 36.6823529412,
"line_max": 76,
"alpha_frac": 0.6625039026,
"autogenerated": false,
"ratio": 3.... |
import numpy as np
from sklearn.utils.testing import assert_warns
from sklearn.utils.testing import assert_raises
from sklearn.utils.testing import assert_almost_equal
from sklearn.utils.testing import assert_array_almost_equal
from sklearn.exceptions import ConvergenceWarning
from sklearn.decomposition import Factor... | {
"repo_name": "chrsrds/scikit-learn",
"path": "sklearn/decomposition/tests/test_factor_analysis.py",
"copies": "1",
"size": "3043",
"license": "bsd-3-clause",
"hash": -8823553646527459000,
"line_mean": 36.1097560976,
"line_max": 76,
"alpha_frac": 0.6523167926,
"autogenerated": false,
"ratio": 3.4... |
__author__ = ('Christian Osendorfer, osendorf@in.tum.de;'
'Justin S Bayer, bayerj@in.tum.de'
'SUN Yi, yi@idsia.ch')
from scipy import random, outer, zeros, ones
from pybrain.datasets import SupervisedDataSet, UnsupervisedDataSet
from pybrain.supervised.trainers import Trainer
from pybrain.... | {
"repo_name": "chanderbgoel/pybrain",
"path": "pybrain/unsupervised/trainers/rbm.py",
"copies": "25",
"size": "6716",
"license": "bsd-3-clause",
"hash": -8331061457445809000,
"line_mean": 33.9791666667,
"line_max": 86,
"alpha_frac": 0.5704288267,
"autogenerated": false,
"ratio": 3.704357418643133... |
__author__ = 'Christian Tamayo'
def loopstart():
timetime = []
while True:
timething = raw_input('What is your time? Enter time, "done", "remove" or "reset"\n')
if timething.upper() == 'DONE':
break
elif timething.upper() == 'RESET':
timetime = []
elif ti... | {
"repo_name": "cjtamayo/time_add",
"path": "time_adder.py",
"copies": "1",
"size": "1537",
"license": "mit",
"hash": 4997344134605839000,
"line_mean": 28.5576923077,
"line_max": 93,
"alpha_frac": 0.485361093,
"autogenerated": false,
"ratio": 3.607981220657277,
"config_test": false,
"has_no_ke... |
__author__ = 'Christian Tamayo'
def validator(listo):
if type(listo) == str:
listo = [listo]
try:
for time in listo:
split = time.split(':')
assert type(int(split[0])) == int
assert type(int(split[1])) == int
assert int(split[1]) < 60
... | {
"repo_name": "cjtamayo/time_add",
"path": "timer.py",
"copies": "1",
"size": "2745",
"license": "mit",
"hash": 7141247075316666000,
"line_mean": 30.6785714286,
"line_max": 88,
"alpha_frac": 0.4735883424,
"autogenerated": false,
"ratio": 3.7094594594594597,
"config_test": false,
"has_no_keywo... |
__author__ = 'Christie'
# http://www.pythonforbeginners.com/python-on-the-web/parsingjson/
import urllib3
import json
import sys
# Nick's techniques
#'{0} {1}'.format('fist', 'second')
#myjsondata = {'name': 'nick', 'school': 'Brooklyn High'}
#'{name} went to {school}'.format(**myjsondata)
def getSchoolsByProgramHigh... | {
"repo_name": "christieewen/nyc-school-choices",
"path": "searchByProgram.py",
"copies": "1",
"size": "1283",
"license": "apache-2.0",
"hash": -6266951710301184000,
"line_mean": 31.075,
"line_max": 266,
"alpha_frac": 0.6921278254,
"autogenerated": false,
"ratio": 2.8259911894273126,
"config_tes... |
"""
Python functions to test the connectivity and performance of the iRODS icommands iput and iget.
"""
import os
import json
import subprocess
import time
from timeit import default_timer as timer
import hashlib
from tqdm import tqdm
import shutil
RED = "\033[31m"
GREEN = "\033[92m"
BLUE = "\033[34m"
DEFA... | {
"repo_name": "chStaiger/iRODS_tests",
"path": "iRODStestFunctions.py",
"copies": "1",
"size": "14131",
"license": "mit",
"hash": 7981208962537639000,
"line_mean": 38.5826330532,
"line_max": 130,
"alpha_frac": 0.6147477178,
"autogenerated": false,
"ratio": 3.502106567534077,
"config_test": true... |
__author__ = 'Christof Pieloth'
import logging
import os
from packbacker.errors import ParameterError
from packbacker.installer import Installer
from packbacker.utils import UtilsUI
class Job(object):
log = logging.getLogger(__name__)
def __init__(self):
self._installers = []
def add_installe... | {
"repo_name": "cpieloth/PackBacker",
"path": "packbacker/job.py",
"copies": "1",
"size": "2453",
"license": "apache-2.0",
"hash": -9005515819418779000,
"line_mean": 30.0632911392,
"line_max": 99,
"alpha_frac": 0.4626987362,
"autogenerated": false,
"ratio": 4.559479553903346,
"config_test": fals... |
__author__ = 'Christof Pieloth'
import logging
import os
from packbacker.pluginloader import BaseClassCondition
from packbacker.pluginloader import PluginLoader
from packbacker.utils import UtilsUI
class Installer(object):
"""Abstract installer with default implementations of pre_install and post_install."""
... | {
"repo_name": "cpieloth/PackBacker",
"path": "packbacker/installer.py",
"copies": "1",
"size": "3384",
"license": "apache-2.0",
"hash": 5834774232013646000,
"line_mean": 28.9557522124,
"line_max": 115,
"alpha_frac": 0.6125886525,
"autogenerated": false,
"ratio": 4.530120481927711,
"config_test"... |
__author__ = 'Christof Pieloth'
import logging
import os
from packbacker.utils import UtilsUI
class Installer(object):
"""Abstract installer with default implementations of pre_install and post_install."""
def __init__(self, name, label):
self.__name = name
self.__label = label
self... | {
"repo_name": "cpieloth/CppMath",
"path": "tools/PackBacker/packbacker/installers/installer.py",
"copies": "1",
"size": "2604",
"license": "apache-2.0",
"hash": 1717855819827563000,
"line_mean": 28.6022727273,
"line_max": 115,
"alpha_frac": 0.6059907834,
"autogenerated": false,
"ratio": 4.5286956... |
__author__ = 'Christof Pieloth'
import logging
import subprocess
class Utils:
log = logging.getLogger(__name__)
@staticmethod
def check_program(program, arg):
try:
subprocess.call([program, arg], stdout=subprocess.PIPE)
return True
except OSError:
Util... | {
"repo_name": "cpieloth/CppMath",
"path": "tools/PackBacker/packbacker/utils.py",
"copies": "2",
"size": "2407",
"license": "apache-2.0",
"hash": 5665650804013992000,
"line_mean": 25.7555555556,
"line_max": 67,
"alpha_frac": 0.5575405069,
"autogenerated": false,
"ratio": 4.018363939899833,
"con... |
__author__ = 'Christof Pieloth'
import logging
from packbacker.errors import ParameterError
from packbacker.installers import installer_prototypes
from packbacker.utils import UtilsUI
class Job(object):
log = logging.getLogger(__name__)
def __init__(self):
self._installers = []
def add_instal... | {
"repo_name": "cpieloth/CppMath",
"path": "tools/PackBacker/packbacker/job.py",
"copies": "1",
"size": "2389",
"license": "apache-2.0",
"hash": -683360499102689500,
"line_mean": 29.641025641,
"line_max": 99,
"alpha_frac": 0.4566764337,
"autogenerated": false,
"ratio": 4.65692007797271,
"config_... |
__author__ = 'Christof Pieloth'
import os
from subprocess import call
from packbacker.constants import Parameter
from packbacker.errors import ParameterError
from packbacker.utils import Utils
from packbacker.utils import UtilsUI
from packbacker.installer import Installer
class CxxTest(Installer):
"""
Downl... | {
"repo_name": "cpieloth/PackBacker",
"path": "packbacker/installers/cxxtest.py",
"copies": "1",
"size": "2512",
"license": "apache-2.0",
"hash": -2255686142027108400,
"line_mean": 29.2771084337,
"line_max": 79,
"alpha_frac": 0.6277866242,
"autogenerated": false,
"ratio": 3.806060606060606,
"con... |
__author__ = 'Christoph Ehlen'
import sys
import getopt
from database import Database
from operations import do_anonymization, do_cleanup, do_user_cut, do_blur, do_k_anonymity
validOps = ["cleanup", "anonymization", "haircut", "blur", "k_anonymity"]
callOps = {
"cleanup": lambda db, args: do_cleanup(db),
"ano... | {
"repo_name": "Institute-Web-Science-and-Technologies/LiveGovWP1",
"path": "server/DbAnonymization/dbops/app.py",
"copies": "1",
"size": "2511",
"license": "mit",
"hash": 7152942116458740000,
"line_mean": 29.6341463415,
"line_max": 89,
"alpha_frac": 0.5802469136,
"autogenerated": false,
"ratio": ... |
"""
A module to ease the creation of card games in written in python
The Card class is the representation of a single card, including it's
image and the back image.
The Stack class is the representation of a stack of playing cards.
To Use:
from playingcards import Card
from playingcards import Stack
"""
import os.... | {
"repo_name": "ccdale/playingcards",
"path": "playingcards.py",
"copies": "1",
"size": "7319",
"license": "mit",
"hash": -7293436588681191000,
"line_mean": 23.4782608696,
"line_max": 118,
"alpha_frac": 0.6358792185,
"autogenerated": false,
"ratio": 3.5702439024390245,
"config_test": false,
"h... |
import os
import logging
import pandas as pd
from math import ceil, pi, exp, log, sqrt
from pyproj import Proj
import numpy as np
from collections import defaultdict
logging.basicConfig(format='%(asctime)s\t\t%(message)s', level=logging.DEBUG)
# general
LHV_DIESEL = 9.9445485 # (kWh/l) lower heating value
HOURS_PER... | {
"repo_name": "KTH-dESA/PyOnSSET-jupyter",
"path": "onsset.py",
"copies": "1",
"size": "54327",
"license": "mit",
"hash": -397660734843026600,
"line_mean": 48.9329044118,
"line_max": 120,
"alpha_frac": 0.5694958308,
"autogenerated": false,
"ratio": 3.3568339100346023,
"config_test": false,
"h... |
__author__ = 'Christopher Bock'
from LoggingClass import LoggingClass
class RatioHistogram(LoggingClass):
"""
A convenience class to make drawing ratio histograms in ROOT (http://root.cern.ch) easier, especially when dealing
with many histograms in the same plot at once. You can either add histograms pre... | {
"repo_name": "ChristopherBock/pyUtilityClasses",
"path": "UtilityClasses/RatioHistogram.py",
"copies": "1",
"size": "14298",
"license": "mit",
"hash": -3061960387643353000,
"line_mean": 42.4589665653,
"line_max": 180,
"alpha_frac": 0.59434886,
"autogenerated": false,
"ratio": 3.8580679978413386,... |
__author__ = 'Christopher Bock'
class LoggingClass(object):
"""
Serves as base class for classes implementing rudimentary logging functions. In case no logger is supplied to the
constructor, the output will be printed to the console via the print function. If a logger is supplied it will have
to imple... | {
"repo_name": "ChristopherBock/pyUtilityClasses",
"path": "UtilityClasses/LoggingClass.py",
"copies": "1",
"size": "1278",
"license": "mit",
"hash": 8977156897058103000,
"line_mean": 40.2258064516,
"line_max": 119,
"alpha_frac": 0.6236306729,
"autogenerated": false,
"ratio": 4.231788079470198,
... |
__author__ = 'Christopher Fonnesbeck, fonnesbeck@maths.otago.ac.nz'
from pymc.StepMethods import *
class TWalk(StepMethod):
"""
The t-walk is a scale-independent, adaptive MCMC algorithm for arbitrary
continuous distributions and correltation structures. The t-walk maintains two
independent points in ... | {
"repo_name": "matthew-brett/pymc",
"path": "pymc/sandbox/TWalk.py",
"copies": "1",
"size": "2413",
"license": "mit",
"hash": 5659422517132439000,
"line_mean": 34.4852941176,
"line_max": 152,
"alpha_frac": 0.6369664318,
"autogenerated": false,
"ratio": 4.062289562289562,
"config_test": false,
... |
'''Library to manipulate .srt subtitle files. Currently srtTool can shift
subtitles by seconds or change to new frame rates. It also can match film
script files to spotted timecodes. PAL uses a frame rate of 25, while NTSC
uses a frame rate of 29.97. 35mm videos have a frame rate of 24. But transfer
from telecining fo... | {
"repo_name": "henchc/srtTool",
"path": "srt-tool/srt-Shift.py",
"copies": "1",
"size": "4344",
"license": "mit",
"hash": 55928467688551950,
"line_mean": 32.9375,
"line_max": 80,
"alpha_frac": 0.5080570902,
"autogenerated": false,
"ratio": 3.208271787296898,
"config_test": false,
"has_no_keyw... |
'''Working product to automatically generate SRT file from script. Intervals are given as user inputs while watching video. Line breaks are determined by a tree parsing algorithm.'''
import time
from nltk.parse import stanford
from nltk import sent_tokenize, Tree
from string import punctuation
punctuation = punctuat... | {
"repo_name": "henchc/srtTool",
"path": "srt-tool/srt-Script2SRT.py",
"copies": "1",
"size": "4704",
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# Basic tools
import itertools
# Scalers
from sklearn.preprocessing import StandardScaler
from sklearn.preprocessing import Normalizer
from sklearn.preprocessing import MinMaxScaler
from sklearn.preprocessing import Binarizer
# Feature selection tools
from sklearn.feature_selection import SelectKBest, f_classif
# U... | {
"repo_name": "JaggedParadigm/pyplearnr",
"path": "build/lib/pyplearnr/pipeline_builder.py",
"copies": "2",
"size": "7784",
"license": "apache-2.0",
"hash": -2304020637028828700,
"line_mean": 34.7064220183,
"line_max": 101,
"alpha_frac": 0.5381551901,
"autogenerated": false,
"ratio": 4.8680425265... |
import numpy as np
# Classification metrics
import sklearn.metrics as sklearn_metrics
from sklearn.metrics import classification_report
class PipelineEvaluator(object):
"""
Class used to evaluate pipelines
"""
def get_score(self, y, y_pred, scoring_metric):
"""
Returns the score give... | {
"repo_name": "JaggedParadigm/pyplearnr",
"path": "build/lib/pyplearnr/pipeline_evaluator.py",
"copies": "2",
"size": "3758",
"license": "apache-2.0",
"hash": -6960512631214257000,
"line_mean": 31.3965517241,
"line_max": 92,
"alpha_frac": 0.5332623736,
"autogenerated": false,
"ratio": 4.468489892... |
# Python 2/3 compatibility
from __future__ import print_function
# Basic tools
import numpy as np
import pandas as pd
import random
import re
# For scikit-learn pipeline cloning
from sklearn.base import clone
# Graphing
import pylab as plt
import matplotlib
import matplotlib.pyplot as mpl_plt
import matplotlib.col... | {
"repo_name": "JaggedParadigm/pyplearnr",
"path": "pyplearnr/nested_k_fold_cross_validation.py",
"copies": "2",
"size": "48375",
"license": "apache-2.0",
"hash": -7030119948803383000,
"line_mean": 39.2789342215,
"line_max": 151,
"alpha_frac": 0.5459431525,
"autogenerated": false,
"ratio": 4.29389... |
__author__ = 'christopher'
from ase.atoms import Atoms
import ase.io as aseio
from pyiid.calc.calc_1d import Calc1D
from pyiid.utils import build_sphere_np
import matplotlib.pyplot as plt
import time
from copy import deepcopy as dc
from pyiid.experiments.elasticscatter import ElasticScatter
import numpy as np
scat =... | {
"repo_name": "CJ-Wright/pyIID",
"path": "benchmarks/time_comparison.py",
"copies": "1",
"size": "4558",
"license": "bsd-3-clause",
"hash": -593192009839550100,
"line_mean": 30.2191780822,
"line_max": 126,
"alpha_frac": 0.6259324265,
"autogenerated": false,
"ratio": 2.6954464813719694,
"config_... |
__author__ = 'christopher'
def is_coor_formatted(coor):
if "/" in coor:
return True
else:
return False
def format_coor(coor): #used to go from 100 64 100 format to 100 S / 100 N format
x = coor.split()[0]
y = coor.split()[2]
z = coor.split()[1]
if float(x) > 0:
x ... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "util.py",
"copies": "1",
"size": "1381",
"license": "mit",
"hash": 5493508096174041000,
"line_mean": 24.1090909091,
"line_max": 156,
"alpha_frac": 0.5104996379,
"autogenerated": false,
"ratio": 2.78989898989899,
"config_test": fa... |
__author__ = 'christopher'
import parse
import thread
import commands
import memorydb
import playerdb
import logger
import runtime
import event
import util
def route(line):
try:
p = parse.parse_log(line)
if p.type == "Filtered":
pass
if p.type == "GMSG":
logge... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "director.py",
"copies": "1",
"size": "11519",
"license": "mit",
"hash": -4981774707975874000,
"line_mean": 39.1393728223,
"line_max": 140,
"alpha_frac": 0.4792082646,
"autogenerated": false,
"ratio": 4.214782290523234,
"config_te... |
__author__ = 'christopher'
global player_array
global last_airdrop
player_array = []
online_players = []
last_airdrop = ""
airdrops= []
import logger
class player_object(object):
def __init__(self):
self.name = ""
self.entityid = 0
self.steamid = 0
self.ip= ""
self.lastlo... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "memorydb.py",
"copies": "1",
"size": "3368",
"license": "mit",
"hash": 5832777666298886000,
"line_mean": 23.7720588235,
"line_max": 64,
"alpha_frac": 0.5950118765,
"autogenerated": false,
"ratio": 3.4508196721311477,
"config_test... |
__author__ = 'christopher'
import threading
import telnetlib
import director
import runtime
import logger
class telnet_connect_telnetlib(threading.Thread):
def __init__(self, threadID, name, counter):
threading.Thread.__init__(self)
self.threadID = threadID
self.name = name
self... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "telconn.py",
"copies": "1",
"size": "1277",
"license": "mit",
"hash": 4328668558540861000,
"line_mean": 24.0392156863,
"line_max": 64,
"alpha_frac": 0.5293657009,
"autogenerated": false,
"ratio": 4.092948717948718,
"config_test":... |
__author__ = 'christopher'
from Tkinter import *
from ttk import *
import memorydb
import telconn
import threading
import logger
import event
import runtime
import time
import config
selected_player = ""
def toggle_verbose():
if verbose_chk.get() == 1:
runtime.verbose = True
else:
runtime... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "gui.py",
"copies": "1",
"size": "9881",
"license": "mit",
"hash": -7036192003446827000,
"line_mean": 28.4077380952,
"line_max": 115,
"alpha_frac": 0.6870762069,
"autogenerated": false,
"ratio": 2.8410005750431284,
"config_test": ... |
__author__ = 'christopher'
#parses the log and returns a parsed_log object
import re
import string
import memorydb
import logger
from time import strftime
class ParsedLog(object):
def __init__(self):
self.type = ""
self.event = ""
self.full_text = ""
def parse_log(line):
pl = Par... | {
"repo_name": "christopher-roelofs/7dtd-server-manager",
"path": "parse.py",
"copies": "1",
"size": "12491",
"license": "mit",
"hash": 6345382945512915000,
"line_mean": 32.044973545,
"line_max": 240,
"alpha_frac": 0.4628132255,
"autogenerated": false,
"ratio": 3.905878674171357,
"config_test": ... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import glob
import os
import cv2
import numpy as np
import re
import matplotlib.pyplot as plt
import matplotlib
import time
from mpl_toolkits.axes_grid1 import make_axes_locatable
from sensor_correction.gp_cpu import ... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/correct_depth.py",
"copies": "1",
"size": "3179",
"license": "bsd-3-clause",
"hash": -5414958977407274000,
"line_mean": 31.1111111111,
"line_max": 111,
"alpha_frac": 0.5920100661,
"autogenerated": false,
"ratio": 3.3855165069... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import glob
import os
import cv2
import numpy as np
import re
def model_points(pattern):
corners = np.zeros((pattern[0]*pattern[1], 3), dtype=np.float32)
for i in range(pattern[1]):
for j in range(patt... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/depth_from_pattern.py",
"copies": "1",
"size": "4335",
"license": "bsd-3-clause",
"hash": 6990403979453158000,
"line_mean": 34.5327868852,
"line_max": 144,
"alpha_frac": 0.5760092272,
"autogenerated": false,
"ratio": 3.072289... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import glob
import os
import numpy as np
import matplotlib.pyplot as plt
from sensor_correction.utils import sensor_unproject
from sensor_correction.gp_cpu import GPRegressor
def select_data(temps, poses, all_depths_... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/train.py",
"copies": "1",
"size": "3582",
"license": "bsd-3-clause",
"hash": -3197969751457897000,
"line_mean": 30.1565217391,
"line_max": 142,
"alpha_frac": 0.5642099386,
"autogenerated": false,
"ratio": 3.1896705253784505,
... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import glob
import os
import numpy as np
import re
import matplotlib.pyplot as plt
import matplotlib
from sensor_correction.utils import mask_outliers
from sensor_correction.utils import sensor_unproject
import seabo... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/plot_statistics.py",
"copies": "1",
"size": "3208",
"license": "bsd-3-clause",
"hash": -729235003929808400,
"line_mean": 33.4946236559,
"line_max": 105,
"alpha_frac": 0.6006857855,
"autogenerated": false,
"ratio": 3.135874877... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sn
from collections import defaultdict
import seaborn as sbn
sbn.set_context('paper')
sbn.set(font_scale=2)
if __name__ == '__main__':
import ... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/plot_depth_vs_temperature.py",
"copies": "1",
"size": "3538",
"license": "bsd-3-clause",
"hash": 4978591717838028000,
"line_mean": 35.8333333333,
"line_max": 120,
"alpha_frac": 0.5777714932,
"autogenerated": false,
"ratio": 3... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import numpy as np
import pandas as pd
import os
import matplotlib.pyplot as plt
import cv2
def crop(img, border):
return img[border[1]:-border[1], border[0]:-border[0]]
if __name__ == '__main__':
import arg... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/preprocess_depth.py",
"copies": "1",
"size": "2503",
"license": "bsd-3-clause",
"hash": 8982445481966697000,
"line_mean": 35.2898550725,
"line_max": 125,
"alpha_frac": 0.5713144227,
"autogenerated": false,
"ratio": 3.30211081... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import numpy as np
import tensorflow as tf
from tensorflow.contrib.staging import StagingArea
from tensorflow.python.ops import data_flow_ops
import time
import math
from sensor_correction.gp_cpu import GPRegressor
f... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/compare_cpu_gpu.py",
"copies": "1",
"size": "2914",
"license": "bsd-3-clause",
"hash": 854781091558726800,
"line_mean": 32.8953488372,
"line_max": 124,
"alpha_frac": 0.5868222375,
"autogenerated": false,
"ratio": 3.3113636363... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import numpy as np
import tensorflow as tf
class GPRegressorGPU:
'''Gaussian Process regressor on GPU.
Takes a pre-fitted Gaussian Process regressor (CPU) and prepares a TensorFlow graph for
prediction us... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/gp_gpu.py",
"copies": "1",
"size": "1725",
"license": "bsd-3-clause",
"hash": 3465722650811564500,
"line_mean": 30.3636363636,
"line_max": 91,
"alpha_frac": 0.5785507246,
"autogenerated": false,
"ratio": 3.409090909090909,
"conf... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import numpy as np
from sklearn.gaussian_process import GaussianProcessRegressor
from sklearn.gaussian_process.kernels import RBF, ConstantKernel, WhiteKernel
from sklearn.externals import joblib
class GPRegressor:
... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/gp_cpu.py",
"copies": "1",
"size": "5241",
"license": "bsd-3-clause",
"hash": -1424031738002748000,
"line_mean": 32.8129032258,
"line_max": 142,
"alpha_frac": 0.5956878458,
"autogenerated": false,
"ratio": 3.6753155680224405,
"c... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
import pandas as pd
import numpy as np
import glob
import re
import os
if __name__ == '__main__':
import argparse
parser = argparse.ArgumentParser(description='Convert raw image files to pandas csv with uniqu... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/create_pandas.py",
"copies": "1",
"size": "1178",
"license": "bsd-3-clause",
"hash": 1572360680582106400,
"line_mean": 28.45,
"line_max": 114,
"alpha_frac": 0.5483870968,
"autogenerated": false,
"ratio": 3.56969696969697,
"... |
__author__ = 'Christoph Heindl'
__copyright__ = 'Copyright 2017, Profactor GmbH'
__license__ = 'BSD'
'''
Converts raw caputered 4D data (temperature, axis-position, color, depth) from @gebenh capture tool to
a list of unique filenames matching the following pattern
<id>_t<temperature>_p<axisposition>_<color|depth... | {
"repo_name": "cheind/rgbd-correction",
"path": "sensor_correction/apps/convert.py",
"copies": "1",
"size": "1598",
"license": "bsd-3-clause",
"hash": 2787575978822131000,
"line_mean": 38,
"line_max": 116,
"alpha_frac": 0.6464330413,
"autogenerated": false,
"ratio": 3.451403887688985,
"config_t... |
__author__ = 'Christoph Jansen, HTW Berlin'
from data import CorpusOptions
def accuracy(flags):
c = correct(flags)
w = wrong(flags)
if c == 0:
return 0
return c / (c + w)
def correct(flags):
tn, fp, tp, fn = flags
return tp + tn
def wrong(flags):
tn, fp, tp, fn = flags
return... | {
"repo_name": "Gnork/confusion-words",
"path": "transformation_based_rule_learning/scores.py",
"copies": "1",
"size": "4072",
"license": "mit",
"hash": 6662285926998216000,
"line_mean": 24.7784810127,
"line_max": 83,
"alpha_frac": 0.5790766208,
"autogenerated": false,
"ratio": 3.129900076863951,
... |
__author__ = 'Christoph Jansen, HTW Berlin'
import copy
class TokenOrPOS:
def __init__(self, value: str, isToken: bool):
self.value = value
self.isToken = isToken
class Collocations:
def __init__(self, check_sequence: [TokenOrPOS], k: int, origin_token: str, replace_token: str):
self.... | {
"repo_name": "Gnork/confusion-words",
"path": "transformation_based_rule_learning/rule_templates.py",
"copies": "1",
"size": "8572",
"license": "mit",
"hash": 4163761751442372600,
"line_mean": 34.7208333333,
"line_max": 184,
"alpha_frac": 0.5531964536,
"autogenerated": false,
"ratio": 3.86997742... |
__author__ = 'Christoph Jansen, HTW Berlin'
import os
import normalization
import pickle
def export_rule_set(path, rule_set):
with open(path, 'wb') as f:
pickle.dump(rule_set, f)
def import_rule_set(path):
with open(path, 'rb') as f:
rule_set = pickle.load(f)
return rule_set
class TSVDat... | {
"repo_name": "Gnork/confusion-words",
"path": "transformation_based_rule_learning/io_wrapper.py",
"copies": "1",
"size": "4741",
"license": "mit",
"hash": 1325073796735393000,
"line_mean": 28.6375,
"line_max": 101,
"alpha_frac": 0.5498839907,
"autogenerated": false,
"ratio": 3.7155172413793105,
... |
__author__ = 'Christoph Jansen, HTW Berlin'
import os
import scores
from data import CorpusOptions
import json
def log_exp_settings(log_path, exp_settings):
open_log = open(log_path, 'w')
data = json.dumps(exp_settings, sort_keys=True)
print(data, file=open_log)
open_log.close()
print(data)
class... | {
"repo_name": "Gnork/confusion-words",
"path": "transformation_based_rule_learning/logger.py",
"copies": "1",
"size": "4521",
"license": "mit",
"hash": -2857388433375327700,
"line_mean": 40.1090909091,
"line_max": 128,
"alpha_frac": 0.5169210352,
"autogenerated": false,
"ratio": 3.576740506329113... |
__author__ = 'Christoph Jansen, HTW Berlin'
import theano
import theano.tensor as T
from theano import dot
from theano.tensor.nnet import sigmoid as sigm
from theano.tensor import tanh
from theano.tensor.nnet import softmax
from theano.tensor.nnet import categorical_crossentropy
import os
import numpy as np
from datet... | {
"repo_name": "Gnork/confusion-words",
"path": "lstm_word2vec_language_model/__main__.py",
"copies": "1",
"size": "11014",
"license": "mit",
"hash": -727890525369080800,
"line_mean": 31.9790419162,
"line_max": 125,
"alpha_frac": 0.5524786635,
"autogenerated": false,
"ratio": 3.1289772727272727,
... |
__author__ = 'Christoph Jansen'
import nltk
import os
from brocas_lm.model import Normalization
from brocas_lm.model import NormalizationIter
from brocas_lm.model import LanguageModel
# create work dir
work_dir = os.path.join(os.path.expanduser('~'), 'brocas_models')
lm_file = os.path.join(work_dir, 'test_model.bin'... | {
"repo_name": "PandoIO/brocas-lm",
"path": "examples/functionality_test.py",
"copies": "1",
"size": "1663",
"license": "mit",
"hash": -9420340432076412,
"line_mean": 26.7333333333,
"line_max": 97,
"alpha_frac": 0.7522549609,
"autogenerated": false,
"ratio": 3.1676190476190476,
"config_test": fa... |
__author__ = 'Christoph Jansen'
import numpy as np
import theano
import theano.tensor as T
from theano import dot
from theano.tensor.nnet import sigmoid as sigm
from theano.tensor import tanh
from theano.tensor.nnet import softmax
from theano.tensor.nnet import categorical_crossentropy
class _LSTM:
de... | {
"repo_name": "PandoIO/brocas-lm",
"path": "brocas_lm/_lstm.py",
"copies": "1",
"size": "5605",
"license": "mit",
"hash": -8051213868425291000,
"line_mean": 39.5333333333,
"line_max": 110,
"alpha_frac": 0.428367529,
"autogenerated": false,
"ratio": 3.044541010320478,
"config_test": false,
"ha... |
__author__ = 'Christoph Jansen'
import os
import pickle
import numpy as np
import theano
from collections import Counter
from datetime import datetime
from brocas_lm._lstm import _LSTM
from gensim.models import Word2Vec
class LanguageModel:
def __init__(self,
verbose=True,
... | {
"repo_name": "PandoIO/brocas-lm",
"path": "brocas_lm/model.py",
"copies": "1",
"size": "10514",
"license": "mit",
"hash": 6736286323381115000,
"line_mean": 36.24,
"line_max": 117,
"alpha_frac": 0.5389005136,
"autogenerated": false,
"ratio": 3.8274481252275208,
"config_test": false,
"has_no_k... |
_dtype_str_translation = { 'int': 'i',
'i': 'i',
'float': 'f',
'f': 'f',
'double': 'd',
'd': 'd',
'ui': 'uint',
'uint': 'uint',
... | {
"repo_name": "classner/fertilized-devtools",
"path": "binding_generator/TypeTranslations.py",
"copies": "2",
"size": "3948",
"license": "bsd-2-clause",
"hash": 2134578903973619500,
"line_mean": 50.9473684211,
"line_max": 156,
"alpha_frac": 0.4295845998,
"autogenerated": false,
"ratio": 3.7315689... |
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