text stringlengths 0 1.05M | meta dict |
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from django.db import models
from django.conf import settings
import django.utils.timezone as timezone
from django.core.exceptions import ValidationError
import uuid
class Author(models.Model):
user = models.OneToOneField(settings.AUTH_USER_MODEL,
on_delete=models.CASCADE)
url... | {
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"path": "dash/models.py",
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from django.http import HttpResponse, JsonResponse, HttpResponseForbidden
from django.shortcuts import render, redirect, get_object_or_404
from django.contrib.auth.decorators import login_required
from django.contrib.auth.mixins import LoginRequiredMixin
from django.views.decorators.http import require_POST, require_G... | {
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"path": "dash/views.py",
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from rest_framework.views import APIView
from dash.models import Post, Author, Category, CanSee
from .serializers import PostSerializer
from .verifyUtils import postValidators, NotFound, ResourceConflict
from .dataUtils import validateData, pidToUrl, getPostData, getPost
from .httpUtils import JSONResponse
class Pos... | {
"repo_name": "CMPUT404W17T06/CMPUT404-project",
"path": "rest/singlePostView.py",
"copies": "1",
"size": "5349",
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from urllib.parse import urlsplit, urlunsplit
from django.core.paginator import Paginator
from rest_framework import serializers
import requests
from dash.models import Post, Author, Comment, Category, CanSee, \
RemoteCommentAuthor
from .models import RemoteCredentials
from .authUtils import ... | {
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"""
Django settings for stream project.
Generated by 'django-admin startproject' using Django 1.10.6.
For more information on this file, see
https://docs.djangoproject.com/en/1.10/topics/settings/
For the full list of settings and their values, see
https://docs.djangoproject.com/en/1.10/ref/settings/
"""
import os... | {
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"""stream URL Configuration
The `urlpatterns` list routes URLs to views. For more information please see:
https://docs.djangoproject.com/en/1.10/topics/http/urls/
Examples:
Function views
1. Add an import: from my_app import views
2. Add a URL to urlpatterns: url(r'^$', views.home, name='home')
Class-bas... | {
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"path": "stream/urls.py",
"copies": "1",
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"hash": -9002874958720669000,
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"autogenerated": false,
"ratio": 3.4864130434782608,
"config_t... |
#TODO: Add test cases
import subprocess, re, datetime
from dateutil.parser import *
#Warning: If ffprobe's output is ever changed, this part might break.
def getLength(filename):
result = subprocess.Popen(["ffprobe", filename],
stdout = subprocess.PIPE, stderr = subprocess.STDOUT)
return [x for x in result.st... | {
"repo_name": "timezombi/lsvd",
"path": "video_duration.py",
"copies": "1",
"size": "1032",
"license": "apache-2.0",
"hash": 8151305958726048000,
"line_mean": 31.25,
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"autogenerated": false,
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"has_no... |
__author__ = 'Brandon C. Kelly'
import numpy as np
import matplotlib.pyplot as plt
from scipy.linalg import solve
from scipy.optimize import minimize
import samplers
import multiprocessing
import _carmcmc as carmcmcLib
class CarmaModel(object):
"""
Class for performing statistical inference assuming a CARMA(... | {
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# MIT License
# Permission is hereby granted, free of charge, to any person obtaining
# a copy of this software and associated documentation files (the
# "Software"), to deal in the Software without restriction, including
# without limitation the rights to use, copy, modify, merge, publish,
# distribute, sublicense, ... | {
"repo_name": "brandonhamilton/updown-python",
"path": "updown/__init__.py",
"copies": "1",
"size": "3415",
"license": "mit",
"hash": -4784523748264064000,
"line_mean": 33.8469387755,
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"alpha_frac": 0.6442166911,
"autogenerated": false,
"ratio": 3.675995694294941,
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__author__ = 'brandonkelly'
from distutils.core import setup, Extension
import numpy.distutils.misc_util
import os
import platform
system_name= platform.system()
#desc = open("README.rst").read()
extension_version = "0.1.0"
extension_url = "https://github.com/bckelly80/big_data_combine"
BOOST_DIR = os.environ["BOOST_... | {
"repo_name": "brandonckelly/BDC",
"path": "HMLinMAE/python/hmlin_mae/setup.py",
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__author__ = 'brandonkelly'
import numpy as np
from numba import jit
import matplotlib.pyplot as plt
from scipy.interpolate import interp1d
import time
@jit # if you don't have number, then comment out this line but this routine will be slow!
def dynamic_time_warping(tseries1, tseries2):
"""
Compute the dyn... | {
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__author__ = 'brandonkelly'
import numpy as np
from sklearn.isotonic import IsotonicRegression
class REACT(object):
def __init__(self, basis='DCT', n_components=None, method='monotone'):
try:
basis.lower() in ['dct', 'manual']
except ValueError:
'Input basis must be eithe... | {
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__author__ = 'brandonkelly'
import numpy as np
import abc
from sklearn.linear_model import LogisticRegression
from sklearn.grid_search import GridSearchCV, ParameterGrid
from sklearn.tree import DecisionTreeClassifier, DecisionTreeRegressor
from sklearn.ensemble import RandomForestClassifier, GradientBoostingClassifi... | {
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"path": "build/lib/bck_stats/sklearn_estimator_suite.py",
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"autogenerated": false,
"ratio": 4.035283474065... |
__author__ = 'brandonkelly'
import numpy as np
import carmcmc as cm
import matplotlib.pyplot as plt
from os import environ
import cPickle
from astropy.io import fits
import multiprocessing
from matplotlib.mlab import detrend_mean
base_dir = environ['HOME'] + '/Projects/carma_pack/src/paper/'
data_dir = base_dir + 'd... | {
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"path": "src/paper/carma_paper.py",
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"autogenerated": false,
"ratio": 2.78490298154283,
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__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
from os import environ
from scipy.misc import comb
import carmcmc
# true values
p = 5 # order of AR polynomial
sigmay = 2.3
qpo_width = np.array([1.0/100.0, 1.0/100.0, 1.0/500.0])
qpo_cent = np.array([1.0/5.0, 1.0/50.0])
ar_roots = carmcm... | {
"repo_name": "farr/carma_pack",
"path": "cpp_tests/analyze_test_data.py",
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"size": "2137",
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__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
from sklearn.neighbors import NearestNeighbors
from scipy.spatial.distance import cdist
from scipy import linalg
import multiprocessing
def distance_matrix(Xvals):
covar = np.cov(Xvals, rowvar=0)
covar_inv = linalg.inv(covar)
... | {
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"autogenerated": false,
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__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
import lib_hmlinmae as maeLib
import yamcmcpp
class LinMAESample(yamcmcpp.MCMCSample):
def __init__(self, y, X):
super(LinMAESample, self).__init__()
self.y = y
self.X = X
self.mfeat = X[0].shape[1]
... | {
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"path": "HMLinMAE/hmlin_mae.py",
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"ratio": 3.2384690873405297,
"config_test": false,
... |
__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
import os
# physical constants, cgs
clight = 2.99792458e10
hplanck = 6.6260755e-27
kboltz = 1.380658e-16
wavelength = np.asarray([100.0, 160.0, 250.0, 350.0, 500.0]) # observational wavelengths in microns
nu = clight / (wavelength / 1e4)... | {
"repo_name": "brandonckelly/CUDAHM",
"path": "dusthm/src/python/make_dusthm_data.py",
"copies": "1",
"size": "2257",
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"confi... |
__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
import pandas as pd
import hmlinmae_gibbs as hmlin
import os
import multiprocessing as mp
from sklearn.ensemble import GradientBoostingRegressor
from sklearn import cross_validation
from sklearn.metrics import mean_absolute_error
import cPi... | {
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"path": "boost_hmlin_residuals.py",
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"size": "9181",
"license": "mit",
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"autogenerated": false,
"ratio": 3.0964586846543,
"config_test": false,
... |
__author__ = 'brandonkelly'
import numpy as np
import matplotlib.pyplot as plt
class GcvExpSmoother(object):
def __init__(self, lookback=30):
"""
Constructor for class to perform exponentially-weighted average smoothing of a 1-D data set.
@param lookback: The maximum look-back length to ... | {
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"ratio": 3.4505229283990344,
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__author__ = 'brandonkelly'
import numpy as np
import pandas as pd
import os
base_dir = os.environ['HOME'] + '/Projects/Kaggle/big_data_combine/'
def boxcox(x):
if np.any(x < 0):
u = x
elif np.any(x == 0):
lamb = 0.5
u = (x ** lamb - 1.0) / lamb
else:
u = np.log(x)
r... | {
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"path": "get_data.py",
"copies": "1",
"size": "1654",
"license": "mit",
"hash": 4036594477273187300,
"line_mean": 26.5833333333,
"line_max": 88,
"alpha_frac": 0.5562273277,
"autogenerated": false,
"ratio": 2.958855098389982,
"config_test": false,
"has_no_key... |
__author__ = 'brandonkelly'
import unittest
import numpy as np
from scipy import stats, integrate
from tree import *
import matplotlib.pyplot as plt
from test_tree_parameters import build_test_data, SimpleBartStep
class ProposalTestCase(unittest.TestCase):
def setUp(self):
nsamples = 500
nfeature... | {
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"path": "tests/test_bart_proposal.py",
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"autogenerated": false,
"ratio": 3.8381147540983607,
"config_test": true,
... |
__author__ = 'brandonkelly'
import unittest
import numpy as np
from scipy import stats, integrate
from tree import *
import matplotlib.pyplot as plt
# generate test data from an ensemble of trees
def build_test_data(X, sigsqr, ngrow=5, mtrees=1):
if np.isscalar(ngrow):
ngrow = [ngrow] * mtrees
ytemp... | {
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"path": "tests/test_tree_parameters.py",
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"ratio": 3.2774977764601245,
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__author__ = 'brandonkelly'
__notes__ = "Adapted from Dan Foreman-Mackey triangle.py module."
import numpy as np
import matplotlib.pyplot as plt
from matplotlib.ticker import MaxNLocator
def multiclass_triangle(xs, classes, labels=None, verbose=True, fig=None, **kwargs):
# Deal with 1D sample lists.
xs = np.... | {
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__author__ = 'brcinko'
from django.conf.urls import patterns, include, url
from django.contrib import admin
from rest_framework.urlpatterns import format_suffix_patterns
import views
urlpatterns = patterns('',
url(r'^$', views.index, name='index'),
url(r'^admin/', includ... | {
"repo_name": "erigones/api_squid",
"path": "urls.py",
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"size": "1446",
"license": "bsd-3-clause",
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"has_no_key... |
__author__ = 'brcinko'
import os
import psycopg2
import psycopg2.extras
import json
import fileinput
import string
from settings import *
"""
This file contains method to update a reconfigure squid proxy server
"""
"""
example of JSON in aclrule.values:
{"values":[
"192.168.0.0/24",
"127.0.0.0/24"
]
}
EVERY R... | {
"repo_name": "erigones/api_squid",
"path": "helpers.py",
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"size": "3668",
"license": "bsd-3-clause",
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"alpha_frac": 0.5932388222,
"autogenerated": false,
"ratio": 3.3962962962962964,
"config_test": false,
"... |
__author__ = 'breddels'
# due to 32 bit limitations in numpy, we cannot use astropy's fits module for writing colfits
import sys
import math
import vaex.dataset
import astropy.io.fits
import numpy as np
import logging
logger = logging.getLogger("vaex.file.colfits")
def empty(filename, length, column_names, data_type... | {
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"path": "packages/vaex-core/vaex/file/colfits.py",
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__author__ = 'breddels'
from ctypes import *
import h5py
import sys
import numpy as np
import mmap
import vaex
import vaex.vaexfast
import timeit
import threading
filename = sys.argv[1]
h5file = h5py.File(filename, "r")
column = h5file[sys.argv[2]]
length = len(column)
assert column.dtype == np.float64
offset = colu... | {
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"path": "bin/vaex_benchmark_mmap.py",
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"autogenerated": false,
"ratio": 2.676207513416816,
"config_test": false,
"... |
__author__ = 'breddels'
import javaobj
import sys
import io
if __name__ == "__main__":
import logging
javaobj._log.setLevel(logging.DEBUG)
jobj = file(sys.argv[1]).read()[16 + 5:]
print((repr(jobj[:100])))
pobj, index = javaobj.loads(jobj)
rest = jobj[index:]
import zlib
print((repr(re... | {
"repo_name": "maartenbreddels/vaex",
"path": "packages/vaex-ui/vaex/ui/gbin.py",
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"autogenerated": false,
"ratio": 3.206997084548105,
"config_te... |
__author__ = 'breddels'
import logging
logger = logging.getLogger("vaex.file")
opener_classes = []
normal_open = open
def register(cls):
opener_classes.append(cls)
import vaex.file.other
try:
import vaex.hdf5 as hdf5
except ImportError:
hdf5 = None
if hdf5:
import vaex.hdf5.dataset
def can_open(path... | {
"repo_name": "maartenbreddels/vaex",
"path": "packages/vaex-core/vaex/file/__init__.py",
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"c... |
__author__ = 'breddels'
"""
Demonstrates combining Qt and tornado, both which want to have their own event loop.
The solution is to run tornado in a thread, the issue is that callbacks will then also be executed in this thread, and Qt doesn't like that.
To fix this, I show how to use execute the callback in the main th... | {
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__author__ = 'breddels'
import unittest
import vaex as vx
import vaex.utils
import vaex.image
import numpy as np
default_size = 2
default_shape = (default_size, default_size)
class TestImage(unittest.TestCase):
def test_blend(self):
black = vaex.image.background(default_shape, "black")
white = vae... | {
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"copies": "1",
"size": "1519",
"license": "mit",
"hash": 955847813265572400,
"line_mean": 28.7843137255,
"line_max": 75,
"alpha_frac": 0.5911784068,
"autogenerated": false,
"ratio": 3.273706896551724,
"config_... |
__author__ = 'Brenda'
from egat.testset import SequentialTestSet
from webdriver_resource import WebDriverResource
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
import time
from egat.execution_groups import execution_group
from selenium.webdriver.common.action_chains import ActionChains... | {
"repo_name": "scotlowery/egat",
"path": "examples/example_amazon/trade_in.py",
"copies": "2",
"size": "5181",
"license": "mit",
"hash": -2070476349811414500,
"line_mean": 39.8031496063,
"line_max": 108,
"alpha_frac": 0.5651418645,
"autogenerated": false,
"ratio": 3.8664179104477614,
"config_te... |
__author__ = 'Brenda'
from egat.testset import SequentialTestSet
from webdriver_resource import WebDriverResource
from selenium import webdriver
import time
class Test5(SequentialTestSet):
def testStep1(self):
# We can access the configuration parameters from inside any test function.
base_url = s... | {
"repo_name": "scotlowery/egat",
"path": "examples/example_jqueryui/toggle_class.py",
"copies": "2",
"size": "2093",
"license": "mit",
"hash": -8654669389129581000,
"line_mean": 36.3928571429,
"line_max": 105,
"alpha_frac": 0.65169613,
"autogenerated": false,
"ratio": 3.883116883116883,
"config... |
__author__ = 'Brenda'
from egat.testset import SequentialTestSet
from webdriver_resource import WebDriverResource
from selenium import webdriver
class Test6(SequentialTestSet):
def testStep1(self):
# We can access the configuration parameters from inside any test function.
base_url = self.configur... | {
"repo_name": "egineering-llc/egat",
"path": "examples/example_jqueryui/selectmenu.py",
"copies": "2",
"size": "3332",
"license": "mit",
"hash": 8634309000428778000,
"line_mean": 41.7307692308,
"line_max": 109,
"alpha_frac": 0.6278511405,
"autogenerated": false,
"ratio": 3.677704194260486,
"con... |
__author__ = 'brendan'
import endpoints
from protorpc import message_types
from protorpc import messages
from google.appengine.ext import ndb
DEBUG = True
USER_AUTH_RC = endpoints.ResourceContainer(message_types.VoidMessage,
email=messages.StringField(1, required=True),
... | {
"repo_name": "boneil3/hyperAdmit",
"path": "backend/utils.py",
"copies": "2",
"size": "1692",
"license": "mit",
"hash": -7233083410390446000,
"line_mean": 43.5526315789,
"line_max": 93,
"alpha_frac": 0.5561465721,
"autogenerated": false,
"ratio": 4.524064171122995,
"config_test": false,
"has... |
__author__ = 'brendan'
import Quandl
import pandas as pd
#secs = ['EURUSD', 'GBPUSD', 'EURGBP', 'AUDUSD', 'USDMXN', 'USDINR', 'USDBRL', 'USDCAD', 'USDZAR']
#datas = []
#for i, sec in enumerate(secs):
# data = pd.DataFrame(Quandl.get('CURRFX/' + sec, authtoken='ZoAeCkDnkL4oFQs1z2_u')['Rate'])
# data = data.loc['2... | {
"repo_name": "boneil3/backtest",
"path": "data.py",
"copies": "1",
"size": "6251",
"license": "mit",
"hash": 8071876989895362000,
"line_mean": 56.3486238532,
"line_max": 118,
"alpha_frac": 0.6938089906,
"autogenerated": false,
"ratio": 2.1971880492091387,
"config_test": false,
"has_no_keywor... |
__author__ = 'Brendan'
import sys
import os
sys.path.append(os.path.join(os.path.dirname(__file__), "lib"))
from endpoints_proto_datastore.ndb.model import EndpointsModel
from endpoints_proto_datastore.ndb.properties import EndpointsAliasProperty
from endpoints_proto_datastore.ndb import EndpointsDateTimeProperty
from ... | {
"repo_name": "Yury191/hyperAdmit",
"path": "backend/models.py",
"copies": "2",
"size": "6715",
"license": "mit",
"hash": 4853666161414979000,
"line_mean": 36.3055555556,
"line_max": 117,
"alpha_frac": 0.6731198809,
"autogenerated": false,
"ratio": 3.8837478311162523,
"config_test": false,
"h... |
__author__ = 'Brendan'
import sys
sys.path.insert(0, 'lib')
sys.path.insert(0, 'stripe')
from webapp2_extras.auth import InvalidPasswordError, InvalidAuthIdError
from protorpc import remote
from backend.models import AdmissionsOfficer
from backend.models import User
from backend.models import FreeUser
from backend.util... | {
"repo_name": "Yury191/hyperAdmit",
"path": "backend/endpoint_classes.py",
"copies": "2",
"size": "6823",
"license": "mit",
"hash": -2486036300456606700,
"line_mean": 45.7328767123,
"line_max": 132,
"alpha_frac": 0.5846401876,
"autogenerated": false,
"ratio": 3.811731843575419,
"config_test": f... |
__author__ = 'brendan'
import helper_functions
import networkx as nx
import matplotlib.pyplot as plt
import datetime
from operator import itemgetter
import numpy as np
import os
proj_cwd = os.path.dirname(os.getcwd())
data_dir = proj_cwd + r'/data'
###############
# BUILD A GRAPH
###############
# Load data from the... | {
"repo_name": "brschneidE3/LegalNetworks",
"path": "python_code/main.py",
"copies": "1",
"size": "3440",
"license": "mit",
"hash": -8358682998653153000,
"line_mean": 35.5957446809,
"line_max": 120,
"alpha_frac": 0.6220930233,
"autogenerated": false,
"ratio": 3.310875842155919,
"config_test": fa... |
__author__ = 'brendan'
import helper_functions
import os
import csv
import datetime
import matplotlib.pyplot as plt
import networkx as nx
proj_cwd = os.path.dirname(os.getcwd())
data_dir = proj_cwd + r'/data'
def consolidate(court_name):
"""
Given court_name, a string representing a CourtListener court, con... | {
"repo_name": "brschneidE3/LegalNetworks",
"path": "python_code/consolidate_data.py",
"copies": "1",
"size": "5733",
"license": "mit",
"hash": 8513154163254786000,
"line_mean": 35.5159235669,
"line_max": 119,
"alpha_frac": 0.5954997384,
"autogenerated": false,
"ratio": 3.3983402489626555,
"conf... |
__author__ = 'brendan'
import helper_functions
import os
import tarfile
proj_cwd = os.path.dirname(os.getcwd())
data_dir = proj_cwd + r'/data'
def download_url(url, destination_path, curl_path=r'C:/Users/brendan/Downloads/curl-7.38.0-win64/bin/curl'):
"""
This is a quick and easy function that simulates cli... | {
"repo_name": "brschneidE3/LegalNetworks",
"path": "python_code/download_data_batch.py",
"copies": "1",
"size": "5208",
"license": "mit",
"hash": -3995542414378783000,
"line_mean": 41.6885245902,
"line_max": 119,
"alpha_frac": 0.6401689708,
"autogenerated": false,
"ratio": 3.329923273657289,
"c... |
__author__ = 'brendan'
import json
from webapp2_extras.appengine.auth.models import UserToken
from webapp2_extras.auth import InvalidAuthIdError
from webapp2_extras.auth import InvalidPasswordError
from backend.models import User
from backend.basehandlers import BaseHandler
import sys
sys.path.insert(0, 'stripe')
im... | {
"repo_name": "boneil3/hyperAdmit",
"path": "backend/auth.py",
"copies": "2",
"size": "6302",
"license": "mit",
"hash": -9197112624926563000,
"line_mean": 31.4896907216,
"line_max": 96,
"alpha_frac": 0.5547445255,
"autogenerated": false,
"ratio": 4.198534310459694,
"config_test": false,
"has_... |
__author__ = 'brendan'
import main
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
import random
import itertools
import time
import dateutil
from datetime import timedelta
cols = ['BoP FA Net', 'BoP FA OI Net', 'BoP FA PI Net', 'CA % GDP']
raw_data = pd... | {
"repo_name": "boneil3/backtest",
"path": "BoP.py",
"copies": "1",
"size": "3114",
"license": "mit",
"hash": -5144292381117619000,
"line_mean": 36.987804878,
"line_max": 108,
"alpha_frac": 0.6380860629,
"autogenerated": false,
"ratio": 2.1945031712473573,
"config_test": false,
"has_no_keyword... |
__author__ = 'brendan'
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
from main import Backtest
import random
import itertools
import time
import dateutil
import sys
sys.path.append('raw_data')
raw_data = pd.read_csv('raw_data/npr_history.csv')
data =... | {
"repo_name": "boneil3/backtest",
"path": "tic.py",
"copies": "1",
"size": "2796",
"license": "mit",
"hash": -4084143796034819000,
"line_mean": 41.3787878788,
"line_max": 120,
"alpha_frac": 0.6552217454,
"autogenerated": false,
"ratio": 3.022702702702703,
"config_test": false,
"has_no_keyword... |
__author__ = 'brendan'
import pandas as pd
import numpy as np
from datetime import datetime as dt
from matplotlib import pyplot as plt
import random
import itertools
import time
import dateutil
import sys
sys.path.append('raw_data')
class Backtest():
def __init__(self):
self.prices = pd.read_csv('raw_da... | {
"repo_name": "boneil3/backtest",
"path": "main.py",
"copies": "1",
"size": "10391",
"license": "mit",
"hash": 8060628764924463000,
"line_mean": 34.7079037801,
"line_max": 117,
"alpha_frac": 0.4849388894,
"autogenerated": false,
"ratio": 3.1893799877225293,
"config_test": false,
"has_no_keywo... |
__author__ = "Brendan O'Connor (anyall.org, brenocon@gmail.com)"
#### Modified by Satish Palaniappan
### Insert Current Path
import os, sys, inspect
cmd_folder = os.path.realpath(os.path.abspath(os.path.split(inspect.getfile(inspect.currentframe()))[0]))
if cmd_folder not in sys.path:
sys.path.insert(0, cmd_folder)
... | {
"repo_name": "tpsatish95/SocialTextFilter",
"path": "Twokenize/emoticons.py",
"copies": "1",
"size": "1928",
"license": "apache-2.0",
"hash": 489043959897261500,
"line_mean": 28.2121212121,
"line_max": 105,
"alpha_frac": 0.6415975104,
"autogenerated": false,
"ratio": 2.342648845686513,
"config... |
__author__ = "Brendan O'Connor (anyall.org, brenocon@gmail.com)"
'''
Copyright 2015 Serendio Inc.
Modified By - Satish Palaniappan
Licensed under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at
http://www.apache.... | {
"repo_name": "tpsatish95/Python-Workshop",
"path": "Python Scripts/social-text-parser/Twokenize/emoticons.py",
"copies": "2",
"size": "2484",
"license": "apache-2.0",
"hash": -4516260468148241000,
"line_mean": 30.8461538462,
"line_max": 168,
"alpha_frac": 0.6751207729,
"autogenerated": false,
"r... |
__author__ = "Brett Bowman"
"""
import ConsensusCore as cc
from BarcodeAnalysis.utils import (arrayFromDataset,
asFloatFeature,
QUIVER_FEATURES)
class ConsensusCoreRead(object):
def __init__(self, bax, holeNum, start, end, chemistry):
s... | {
"repo_name": "bnbowman/BarcodeAnalysis",
"path": "BarcodeAnalysis/ConsensusCoreRead.py",
"copies": "1",
"size": "3102",
"license": "mit",
"hash": 6517154028440666000,
"line_mean": 32.3548387097,
"line_max": 94,
"alpha_frac": 0.6009026435,
"autogenerated": false,
"ratio": 4.018134715025907,
"co... |
__author__ = 'brett'
from monopyly import *
from monopyly.utility import Logger
from monopyly.game.board import Board
from monopyly.squares.property import Property
from monopyly.squares.property_set import PropertySet
from monopyly.squares.station import Station
from monopyly.squares.street import Street
from monopyl... | {
"repo_name": "richard-shepherd/monopyly",
"path": "AIs/Brett Hutley/buffy.py",
"copies": "1",
"size": "45035",
"license": "mit",
"hash": -8352565394576279000,
"line_mean": 37.25913339,
"line_max": 219,
"alpha_frac": 0.5828429304,
"autogenerated": false,
"ratio": 3.6706064558200198,
"config_tes... |
__author__ = 'BrianAguirre'
from DataStructures.Lists.Itr import Itr
from DataStructures.Lists.Itr import Node
class List:
node_list = []
size = len(node_list) - 2
head = Node()
tail = Node()
itr = Itr(head)
node_list.append(head)
node_list.append(tail)
def __init__(self):
... | {
"repo_name": "brianaguirre/SampleCodingInterviews",
"path": "DataStructures/Lists/List.py",
"copies": "1",
"size": "1311",
"license": "mit",
"hash": -2344312290060280300,
"line_mean": 17.7285714286,
"line_max": 50,
"alpha_frac": 0.5675057208,
"autogenerated": false,
"ratio": 3.1820388349514563,
... |
__author__ = 'BrianAguirre'
__twitter__ = 'bnap48'
'''
REQUIREMENTS:
Given three int numbers, calculate which is the most late valid time you can write out of them.
EX: (1, 2, 3, 4) -> 23:41
It has to be valid. If no valid time can be made with the four numbers, return 'NO SOLUTION'
Print should be in format AB:CD, w... | {
"repo_name": "brianaguirre/SampleCodingInterviews",
"path": "ValidLateTimes.py",
"copies": "1",
"size": "5176",
"license": "mit",
"hash": 397480870556093630,
"line_mean": 22.9675925926,
"line_max": 95,
"alpha_frac": 0.4333462133,
"autogenerated": false,
"ratio": 3.3675992192582953,
"config_tes... |
__author__ = 'briana'
import sys
import os
import math
import skimage.io
import skimage.exposure
import numpy as np
SOLAR_IRRADIANCE = {
'LT4': {5: 214.700, 4: 1033.000, 3: 1554.000},
'LT5': {5: 214.900, 4: 1036.000, 3: 1551.000},
'LE7': {5: 225.700, 4: 1044.000, 3: 1547.000}
}
def get_value_from_fil... | {
"repo_name": "zooniverse/kelp",
"path": "import-pipeline/color_calibration.py",
"copies": "1",
"size": "2418",
"license": "apache-2.0",
"hash": -2871293246245683700,
"line_mean": 33.0704225352,
"line_max": 121,
"alpha_frac": 0.6162117452,
"autogenerated": false,
"ratio": 2.959608323133415,
"co... |
from Devices.Input import Input
from Devices.Timer import Timer
from Devices.AnalogInput import AnalogInput
from Devices.Output import Output
class DeviceManager:
def __init__(self):
self.inputs = {}
self.outputs = {}
def addSimpleInput(self, name, location, invert = False):
if name in self.inputs:
raise K... | {
"repo_name": "dillmann/rscs",
"path": "lib/DeviceManager.py",
"copies": "1",
"size": "1683",
"license": "mit",
"hash": -9088539949999225000,
"line_mean": 35.5869565217,
"line_max": 106,
"alpha_frac": 0.7272727273,
"autogenerated": false,
"ratio": 3.455852156057495,
"config_test": false,
"has... |
import re
class Evaluator:
# devices should be a DeviceManager Object
def __init__(self, devices):
self.devices = devices
def evaluate(self, condition):
try:
self.checkLegalExpression(condition)
except:
raise ValueError(e)
# extract device name from quotes in condition
deviceNameExtract = re.find... | {
"repo_name": "dillmann/rscs",
"path": "lib/Graph/Evaluator.py",
"copies": "1",
"size": "1059",
"license": "mit",
"hash": 3869587469151834600,
"line_mean": 29.2571428571,
"line_max": 92,
"alpha_frac": 0.7053824363,
"autogenerated": false,
"ratio": 3.372611464968153,
"config_test": false,
"has... |
__author__ = 'Brian Farrell'
__date_created__ = '1/17/14'
import smtplib
from email.mime.multipart import MIMEMultipart
from email.mime.text import MIMEText
from email.mime.audio import MIMEAudio
from email.mime.base import MIMEBase
from email.mime.image import MIMEImage
from email import encoders
import mimetypes
fr... | {
"repo_name": "vtcgit/ArcPy_Messenger",
"path": "ScriptMessaging.py",
"copies": "1",
"size": "7303",
"license": "mit",
"hash": -4401430514029833700,
"line_mean": 38.2688172043,
"line_max": 164,
"alpha_frac": 0.6115295084,
"autogenerated": false,
"ratio": 4.3756740563211505,
"config_test": false... |
__author__ = 'brianhoffman'
import logging
class Node(object):
def __init__(self, value):
self.value = value
self._child_node = None
# TODO: do note that this is an ordered linked-list. Perhaps this class should
# be renamed to better indicate that.
# TODO: use a generator/iterator pattern... | {
"repo_name": "freebazaar/FreeBazaar",
"path": "rudp/linkedlist.py",
"copies": "3",
"size": "3140",
"license": "mit",
"hash": -289130382915534100,
"line_mean": 28.9047619048,
"line_max": 87,
"alpha_frac": 0.5977707006,
"autogenerated": false,
"ratio": 3.969658659924147,
"config_test": false,
... |
__author__ = "Brian Lenihan <brian.lenihan@gmail.com"
__copyright__ = "Copyright (c) 2012 Python for Android Project"
__license__ = "Apache License, Version 2.0"
import logging
import android
from pyxmpp2.jid import JID
from pyxmpp2.client import Client
from pyxmpp2.settings import XMPPSettings
from pyxmpp2.interface... | {
"repo_name": "louietsai/python-for-android",
"path": "python3-alpha/python3-src/android-scripts/say_chat.py",
"copies": "46",
"size": "2159",
"license": "apache-2.0",
"hash": 5032170297765571000,
"line_mean": 28.5753424658,
"line_max": 85,
"alpha_frac": 0.6956924502,
"autogenerated": false,
"rat... |
__author__ = "Brian Lenihan <brian.lenihan@gmail.com"
__copyright__ = "Copyright (c) 2012 Python for Android Project"
__license__ = "Apache License, Version 2.0"
import logging
import sl4a
from pyxmpp2.jid import JID
from pyxmpp2.client import Client
from pyxmpp2.settings import XMPPSettings
from pyxmpp2.interfaces i... | {
"repo_name": "tomMoulard/python-projetcs",
"path": "scripts3/say_chat.py",
"copies": "1",
"size": "2153",
"license": "apache-2.0",
"hash": -4344941393585683500,
"line_mean": 28.4931506849,
"line_max": 85,
"alpha_frac": 0.6948444032,
"autogenerated": false,
"ratio": 3.570480928689884,
"config_t... |
__author__ = "Brian Lenihan <brian.lenihan@gmail.com"
__copyright__ = "Copyright (c) 2012 Python for Android Project"
__license__ = "Apache License, Version 2.0"
import os
import logging
import android
"""
Create and set a new Tasker variable, display the variable's value in a Tasker
popup, and then clear the variabl... | {
"repo_name": "kmonsoor/python-for-android",
"path": "python3-alpha/python3-src/android-scripts/tasker_example.py",
"copies": "46",
"size": "1883",
"license": "apache-2.0",
"hash": 6468423920425834000,
"line_mean": 28.8888888889,
"line_max": 81,
"alpha_frac": 0.6431226766,
"autogenerated": false,
... |
__author__ = "Brian Lenihan <brian.lenihan@gmail.com"
__copyright__ = "Copyright (c) 2012 Python for Android Project"
__license__ = "Apache License, Version 2.0"
import os
import logging
import sl4a
"""
Create and set a new Tasker variable, display the variable's value in a Tasker
popup, and then clear the variable.
... | {
"repo_name": "tomMoulard/python-projetcs",
"path": "scripts3/tasker_example.py",
"copies": "1",
"size": "1874",
"license": "apache-2.0",
"hash": 3777273291583605000,
"line_mean": 28.746031746,
"line_max": 78,
"alpha_frac": 0.6414087513,
"autogenerated": false,
"ratio": 3.192504258943782,
"conf... |
import math
import numpy as np
import matplotlib.pyplot as plt
import matplotlib as mpl
##############################################################################
# Physical constants
echarge = float("1.6022e-19") # Coloumbs
evjoule = float("1.6022e-19") # eV = ev_to_joule Joules... | {
"repo_name": "brianrlynch85/PlasmaScaling",
"path": "src/electron_collection_reduction.py",
"copies": "1",
"size": "2569",
"license": "mit",
"hash": 6015472095649605000,
"line_mean": 32.8026315789,
"line_max": 79,
"alpha_frac": 0.5165434021,
"autogenerated": false,
"ratio": 3.1755253399258345,
... |
import math
import numpy as np
import matplotlib.pyplot as plt
##############################################################################
# Physical constants
echarge = float("1.6022e-19") # Coloumbs
evjoule = float("1.6022e-19") # eV = ev_to_joule Joules
eperm = float("8.854e-12") # s^4 A^2 m^-3 kg^-1
###... | {
"repo_name": "brianrlynch85/PlasmaScaling",
"path": "src/larmor_dust.py",
"copies": "1",
"size": "2482",
"license": "mit",
"hash": -2438656808747705300,
"line_mean": 36.0447761194,
"line_max": 78,
"alpha_frac": 0.4443996777,
"autogenerated": false,
"ratio": 2.7304730473047303,
"config_test": f... |
import math
import numpy as np
import matplotlib.pyplot as plt
import plasma_parameters as plasma
# Plasma parameters
T_Ar = 0.025 * plasma.evjoule # eV
v_Ar = math.sqrt(T_Ar / plasma.m_Ar) # m s^-1
vt_Ar = math.sqrt(8.0 / math.pi) * v_Ar # m s^-1
##############################... | {
"repo_name": "brianrlynch85/PlasmaScaling",
"path": "src/larmor_ion.py",
"copies": "1",
"size": "2474",
"license": "mit",
"hash": 5927120969690822000,
"line_mean": 31.9866666667,
"line_max": 82,
"alpha_frac": 0.5226354082,
"autogenerated": false,
"ratio": 2.5505154639175256,
"config_test": fal... |
import math
import numpy as np
import matplotlib.pyplot as plt
##############################################################################
# Physical constants
echarge = float("1.6022e-19") # Coloumbs
evjoule = float("1.6022e-19") # eV = ev_to_joule Joules
eperm = float("8.854... | {
"repo_name": "brianrlynch85/PlasmaScaling",
"path": "src/larmor_elec.py",
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from numpy.testing import assert_array_equal, assert_raises
import numpy as np
from scipy.optimize import linear_sum_assignment
def test_linear_sum_assignment():
for cost_matrix, expected_cost in [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
[150,... | {
"repo_name": "Shaswat27/scipy",
"path": "scipy/optimize/tests/test_hungarian.py",
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... |
from numpy.testing import assert_array_equal
from pytest import raises as assert_raises
import numpy as np
from scipy.optimize import linear_sum_assignment
from scipy.sparse.sputils import matrix
def test_linear_sum_assignment():
for sign in [-1, 1]:
for cost_matrix, expected_cost in [
# Sq... | {
"repo_name": "person142/scipy",
"path": "scipy/optimize/tests/test_linear_assignment.py",
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"size": "3150",
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"autogenerated": false,
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import numpy as np
from numpy.testing import assert_array_equal, assert_raises
from clustering_metrics.hungarian import linear_sum_assignment
from clustering_metrics.entropy import assignment_cost
from nose.tools import assert_equal, assert_almost_equal
def test_linear_sum_assignment():
for cost_matrix, expected... | {
"repo_name": "escherba/clustering-metrics",
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"autogenerated": false,
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"c... |
import numpy as np
from numpy.testing import assert_array_equal, assert_raises
from lsh_hdc.hungarian import linear_sum_assignment
from lsh_hdc.entropy import assignment_cost
from nose.tools import assert_equal, assert_almost_equal
def test_linear_sum_assignment():
for cost_matrix, expected_cost in [
# S... | {
"repo_name": "escherba/lsh-hdc",
"path": "tests/test_hungarian.py",
"copies": "2",
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"autogenerated": false,
"ratio": 3.288153681963714,
"config_test":... |
import numpy as np
from numpy.testing import assert_array_equal, assert_raises
from scipy.optimize import linear_sum_assignment
def test_linear_sum_assignment():
for cost_matrix, expected_cost in [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
[150, 4... | {
"repo_name": "DailyActie/Surrogate-Model",
"path": "01-codes/scipy-master/scipy/optimize/tests/test_hungarian.py",
"copies": "1",
"size": "1740",
"license": "mit",
"hash": 576177658038243600,
"line_mean": 28.4915254237,
"line_max": 72,
"alpha_frac": 0.5218390805,
"autogenerated": false,
"ratio":... |
import numpy as np
# XXX we should be testing the public API here
from sklearn.utils.linear_assignment_ import _hungarian
def test_hungarian():
matrices = [
# Square
([[400, 150, 400],
[400, 450, 600],
[300, 225, 300]],
850 # expected cost
),
# Rec... | {
"repo_name": "phdowling/scikit-learn",
"path": "sklearn/utils/tests/test_linear_assignment.py",
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"size": "1349",
"license": "bsd-3-clause",
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"autogenerated": false,
"ratio": 3.5406... |
# TODO #0.23: Remove this test module as the methods being tested
# have been replaced by SciPy methods
import numpy as np
import pytest
@pytest.mark.filterwarnings("ignore::DeprecationWarning")
def test_hungarian():
from sklearn.utils.linear_assignment_ import _hungarian
matrices = [
# Square
... | {
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"path": "sklearn/utils/tests/test_linear_assignment.py",
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"autogenerated": false,
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__author__ = 'Brian M Wilcox'
__version__ = '0.1.3'
"""
Copyright 2014 Brian M Wilcox
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2... | {
"repo_name": "briwilcox/Concurrent-Pandas",
"path": "concurrentpandas.py",
"copies": "1",
"size": "10839",
"license": "apache-2.0",
"hash": -3338542504680345000,
"line_mean": 37.1654929577,
"line_max": 128,
"alpha_frac": 0.5760679029,
"autogenerated": false,
"ratio": 4.320047827819849,
"config... |
__author__ = 'briannelson'
import numpy as np
import pyaudio
import datetime
import sys
class Utilities:
def __init__(self):
"""
Constructor
"""
@staticmethod
def array_from_bytes(data_chunk, sample_width, data_type):
data_length = len(data_chunk)
remainder = data... | {
"repo_name": "SidWatch/pySIDWatch",
"path": "Source/Audio/Utilities.py",
"copies": "1",
"size": "4631",
"license": "mit",
"hash": 4397064531992239000,
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"line_max": 120,
"alpha_frac": 0.5227812567,
"autogenerated": false,
"ratio": 4.368867924528302,
"config_test": tru... |
__author__ = 'briannelson'
import numpy as np
class Logging:
def __init__(self, values_dictionary):
"""
Constructor
"""
self.FilenameFormat = values_dictionary["FilenameFormat"]
self.Folder = values_dictionary["Folder"]
self.TraceLevel = values_dictionary["TraceLeve... | {
"repo_name": "SidWatch/pySIDServerDataProcessor",
"path": "source/SIDServer/Objects.py",
"copies": "1",
"size": "6997",
"license": "mit",
"hash": -7395448491553022000,
"line_mean": 26.4431372549,
"line_max": 87,
"alpha_frac": 0.5370873231,
"autogenerated": false,
"ratio": 3.9089385474860334,
"... |
__author__ = 'briannelson'
import pyaudio
import numpy as np
class Site:
def __init__(self, values_dictionary):
"""
Constructor
"""
self.MonitorId = values_dictionary["MonitorId"]
self.Name = values_dictionary["Name"]
self.Latitude = values_dictionary["Latitude"]
... | {
"repo_name": "SidWatch/pySIDWatch",
"path": "Source/SID/Objects.py",
"copies": "1",
"size": "3323",
"license": "mit",
"hash": -3272782816073355000,
"line_mean": 29.495412844,
"line_max": 113,
"alpha_frac": 0.6136021667,
"autogenerated": false,
"ratio": 4.2657252888318355,
"config_test": false,... |
__author__ = 'briannelson'
import yaml
import io
import h5py
from SID import Objects
import datetime as dt
import math
import numpy as np
from scipy import signal
class DateUtility:
def __init__(self):
"""
Constructor
"""
@staticmethod
def get_next_run_time(current_date_time):
... | {
"repo_name": "SidWatch/pySIDWatch",
"path": "Source/SID/Utilities.py",
"copies": "1",
"size": "6245",
"license": "mit",
"hash": -2843440281644529000,
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"line_max": 100,
"alpha_frac": 0.5681345076,
"autogenerated": false,
"ratio": 4.092398427260813,
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__author__ = 'Brian'
# My first neural net!!!
import math
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data
# Consider implementing feature scaling/whitening (scipy.cluster.vq.whiten?)
# Consi... | {
"repo_name": "bhwester/neural-network",
"path": "neuralnet.py",
"copies": "1",
"size": "18251",
"license": "mit",
"hash": 1912024338889718000,
"line_mean": 43.4063260341,
"line_max": 164,
"alpha_frac": 0.6410607638,
"autogenerated": false,
"ratio": 3.2486650053399786,
"config_test": false,
"... |
__author__ = "Brian O'Neill" # BTO
__doc__ = """
Configurable decorator for debugging and profiling that writes
caller name(s), args+values, function return values, execution time,
number of call, to stdout or to a logger. log_calls can track
call history and provide it in CSV format and Pandas DataFrame format.
NOTE:... | {
"repo_name": "Twangist/log_calls",
"path": "log_calls/log_calls.py",
"copies": "1",
"size": "124843",
"license": "mit",
"hash": -3856040721634263000,
"line_mean": 44.7635630499,
"line_max": 155,
"alpha_frac": 0.5155355126,
"autogenerated": false,
"ratio": 4.200356638180472,
"config_test": fals... |
__author__ = "Brian O'Neill" # BTO
__doc__ = """
Module version = '0.2.4'
Slightly ad-hoc decorator
used_unused_keywords
for `__init__` function of `log_calls`. It's not *totally* ad-hoc:
`log_calls.__init__` only uses half the functionality of this decorator ;/
This decorator allows a function to determine whic... | {
"repo_name": "Twangist/log_calls",
"path": "log_calls/used_unused_kwds.py",
"copies": "1",
"size": "6209",
"license": "mit",
"hash": -6162236203628143000,
"line_mean": 36.1796407186,
"line_max": 97,
"alpha_frac": 0.5872121115,
"autogenerated": false,
"ratio": 3.637375512595196,
"config_test": ... |
__author__ = "Brian O'Neill" # BTO
__doc__ = """
Module version = '0.3.0'
"""
from .deco_settings import DecoSetting, DecoSettingsMapping, DecoSetting_bool
from .log_calls import _deco_base, DecoSettingHistory
from .used_unused_kwds import used_unused_keywords
class record_history(_deco_base):
"""
"""
... | {
"repo_name": "Twangist/log_calls",
"path": "log_calls/record_history.py",
"copies": "1",
"size": "3808",
"license": "mit",
"hash": -5299845316110112000,
"line_mean": 42.2727272727,
"line_max": 121,
"alpha_frac": 0.5701155462,
"autogenerated": false,
"ratio": 3.606060606060606,
"config_test": f... |
__author__ = "Brian O'Neill" # BTO
__version__ = '0.1.14'
__doc__ = """
100% coverage of deco_settings.py
"""
from unittest import TestCase
from log_calls import DecoSetting, DecoSettingsMapping
from log_calls.log_calls import DecoSettingEnabled, DecoSettingHistory
from collections import OrderedDict
import insp... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_deco_settings.py",
"copies": "1",
"size": "24702",
"license": "mit",
"hash": -952965135023368700,
"line_mean": 42.2609457093,
"line_max": 126,
"alpha_frac": 0.5885758238,
"autogenerated": false,
"ratio": 3.9764971023824853,
"config_test": ... |
__author__ = "Brian O'Neill" # BTO
# __version__ = '0.3.0'
__doc__ = """
DecoSettingsMapping -- class that's usable with any class-based decorator
that has several keyword parameters; this class makes it possible for
a user to access the collection of settings as an attribute
(object of type DecoSettingsMapping) of th... | {
"repo_name": "Twangist/log_calls",
"path": "log_calls/deco_settings.py",
"copies": "1",
"size": "27911",
"license": "mit",
"hash": 3632666086308834000,
"line_mean": 41.8082822086,
"line_max": 119,
"alpha_frac": 0.5869012217,
"autogenerated": false,
"ratio": 4.1441722345953975,
"config_test": f... |
__author__ = 'brianoneill'
from log_calls import log_calls
##############################################################################
def test_double_func_deco():
"""
Double-decorating a function doesn't raise:
>>> @log_calls()
... @log_calls()
... def f(): pass
>>> f()
f <== called by <... | {
"repo_name": "Twangist/log_calls",
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"autogenerated": false,
"ratio": 3.564720812182741,
"config_test": ... |
__author__ = 'brianoneill'
from log_calls import log_calls, record_history
import doctest
def test_():
"""
``record_history`` is equivalent to ``log_calls`` with the settings:
record_history=True
log_call_numbers=True
mute=log_calls.MUTE.CALLS
This example, ``f``, doesn't use ``log_message`` or ``log... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_log_calls_as_record_history.py",
"copies": "1",
"size": "2287",
"license": "mit",
"hash": -2907061340422337000,
"line_mean": 29.0921052632,
"line_max": 86,
"alpha_frac": 0.5557498907,
"autogenerated": false,
"ratio": 3.02113606340819,
"con... |
__author__ = 'brianoneill'
from log_calls import record_history
#-----------------------------------------------------
# record_history.print, record_history.print_exprs
# Test in methods, in functions
#-----------------------------------------------------
def test_rh_log_message__output_expected():
"""
-----... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_record_history_log_methods.py",
"copies": "1",
"size": "8533",
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"line_max": 80,
"alpha_frac": 0.4522442283,
"autogenerated": false,
"ratio": 3.5028735632183907,
"co... |
__author__ = 'brianoneill'
import doctest
from log_calls import log_calls
# from log_calls.tests.settings_with_NO_DECO import g_DECORATE, g_settings_dict
from settings_with_NO_DECO import g_DECORATE, g_settings_dict
def test_no_deco__via_dict():
"""
>>> @log_calls(settings=g_settings_dict)
... def f(n, m... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_no_deco__via_dict.py",
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"size": "1618",
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__author__ = 'brianoneill'
import doctest
from log_calls import log_calls
##############################################################################
def test_deco_lambda():
"""
>>> f = log_calls()(lambda x: 2 * x)
>>> f(3)
<lambda> <== called by <module>
arguments: x=3
<lambda> ==> re... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_deco_lambda_cant_deco_callables.py",
"copies": "1",
"size": "3571",
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"hash": 3510669529132237000,
"line_mean": 27.7983870968,
"line_max": 85,
"alpha_frac": 0.5642677121,
"autogenerated": false,
"ratio": 3.5781563126252505,
... |
__author__ = 'brianoneill'
import doctest
from log_calls import log_calls
#-----------------------------------------------------------------------------
def test_dont_decorate__via_file():
"""
>>> @log_calls(settings='settings-with-NO_DECO.txt')
... def f(n, m):
... return 3*n*n*m + 4*n*m*m
... | {
"repo_name": "Twangist/log_calls",
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"config_test... |
__author__ = "Brian O'Neill"
__version__ = '0.3.0'
from log_calls import log_calls
import doctest
#-----------------------------------------------------------------------------
# main__test__get_own_log_calls_wrapper
# test methods accessing their OWN wrappers via utility function/classmethod
# Aiming for complete c... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_get_own_log_calls_wrapper.py",
"copies": "1",
"size": "7870",
"license": "mit",
"hash": -6184044619503666000,
"line_mean": 35.1009174312,
"line_max": 118,
"alpha_frac": 0.493519695,
"autogenerated": false,
"ratio": 3.5482416591523895,
"con... |
__author__ = "Brian O'Neill"
__version__ = '0.3.0'
from log_calls import record_history
import doctest
#-----------------------------------------------------------------------------
# main__record_history_class_deco
#-----------------------------------------------------------------------------
def main__record_histo... | {
"repo_name": "Twangist/log_calls",
"path": "tests/test_record_history__class_deco.py",
"copies": "1",
"size": "5829",
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"hash": 1046654000542091100,
"line_mean": 30.5081081081,
"line_max": 96,
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"autogenerated": false,
"ratio": 3.5051112447384245,
"con... |
"""
getters, setters, adders, deleters in both raw and numeric format?
update method to equivocate raw data and numeric data? Call in read method?
how to deal with NaNs?
"""
import sys
import copy
import operator
import numpy as np
import csv
import analysis
class Data:
# Constructor
def __init__(self, file... | {
"repo_name": "bhwester/computer-science-projects",
"path": "data_analysis_and_visualization_system/data.py",
"copies": "1",
"size": "15719",
"license": "mit",
"hash": 70542518007010830,
"line_mean": 35.6433566434,
"line_max": 127,
"alpha_frac": 0.6071633056,
"autogenerated": false,
"ratio": 3.78... |
import numpy as np
class View:
# constructor
def __init__(self):
# automatically resets the view
self.reset()
def reset(self,
vrp=np.matrix([0.5, 0.5, 1]),
vpn=np.matrix([0, 0, -1]),
vup=np.matrix([0, 1, 0]),
u=np.matrix([-1, 0, 0]... | {
"repo_name": "bhwester/computer-science-projects",
"path": "data_analysis_and_visualization_system/view.py",
"copies": "1",
"size": "6811",
"license": "mit",
"hash": 4936978279960080000,
"line_mean": 38.3757225434,
"line_max": 124,
"alpha_frac": 0.4698282191,
"autogenerated": false,
"ratio": 3.2... |
import numpy as np
import pandas as pd
import sklearn.linear_model as sklearnLinearModel
import sklearn.svm as sklearnSVM
import matplotlib.pyplot as plt
# Read in data
fundedCompanies = pd.read_excel("/Users/Brian/Downloads/cb_data_xlsx_sample.xlsx", \
sheetname="Funded Companies")
rounds = pd.read_excel("/U... | {
"repo_name": "bhwester/computer-science-projects",
"path": "data_analysis_and_visualization_system/finalproject.py",
"copies": "1",
"size": "2965",
"license": "mit",
"hash": 4568199451218487000,
"line_mean": 38.5333333333,
"line_max": 138,
"alpha_frac": 0.7679595278,
"autogenerated": false,
"rat... |
__author__ = 'brock'
"""
Taken from: https://gist.github.com/1094140
"""
from functools import wraps
from flask import request, current_app
from werkzeug.routing import BaseConverter
from werkzeug.exceptions import HTTPException
def jsonp(func):
"""Wraps JSONified output for JSONP requests."""
@wraps(func)
... | {
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"path": "request.py",
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"alpha_frac": 0.6344370861,
"autogenerated": false,
"ratio": 4.184481393507522,
"config_test": false,
"... |
class dstat_plugin(dstat):
def __init__(self):
self.nick = ('read', 'write')
def check(self):
if not os.path.exists('/proc/fs/lustre/llite'):
raise Exception, 'Lustre filesystem not found'
info(1, 'Module %s is still experimental.' % self.filename)
def name(self):
... | {
"repo_name": "SpamapS/dstat-plugins",
"path": "dstat_plugins/plugins/dstat_lustre.py",
"copies": "2",
"size": "1158",
"license": "apache-2.0",
"hash": 877481199610314600,
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"line_max": 104,
"alpha_frac": 0.5483592401,
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... |
""" author: Brogan Ross
A simple to do list app build using kivy.
@todo:
1 - move todo item storage to a better location, ie database, or new internal storage location.
2 - test layouts on mobile devices.
3 - Fix weird layout issues with the PopupDialog's content
Edit Icon from ... | {
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"line_mean": 31.7757575758,
"line_max": 121,
"alpha_frac": 0.5953217456,
"autogenerated": false,
"ratio": 3.9691743119266056,
"config_test": false,
"ha... |
__author__ = 'broglea'
import hashlib
import string
import itertools
def hash_value(type=None, value=None):
if type is None:
return 'You must specify a type'
if value is None:
return 'You must specify a value'
if type == 'MD5':
return hashlib.md5(value).hexdigest()
if type == ... | {
"repo_name": "HackUCF/collabCTF",
"path": "tools/crypto.py",
"copies": "1",
"size": "2974",
"license": "mit",
"hash": 5064461042047061000,
"line_mean": 27.0566037736,
"line_max": 101,
"alpha_frac": 0.5625420309,
"autogenerated": false,
"ratio": 3.986595174262735,
"config_test": false,
"has_n... |
__author__ = 'brooksc'
from pprint import pprint
import requests
import json, time
from requests.auth import HTTPBasicAuth
# import sys
import collections
import json
import time
API_BASE_URL = 'http://www.bugherd.com/api_v2/{api}'
class Error(Exception):
pass
class Response(object):
def __init__(self, bo... | {
"repo_name": "brooksc/bugherd",
"path": "bugherd/__init__.py",
"copies": "1",
"size": "15558",
"license": "mit",
"hash": 8345657850478032000,
"line_mean": 29.5058823529,
"line_max": 233,
"alpha_frac": 0.5903072374,
"autogenerated": false,
"ratio": 3.5103790613718413,
"config_test": false,
"h... |
"""
Check that promotion of read replicas and renaming instances works as expected
"""
import unittest
import time
from boto.rds import RDSConnection
class PromoteReadReplicaTest(unittest.TestCase):
rds = True
def setUp(self):
self.conn = RDSConnection()
self.mainDB_name = "boto-db-%s" % str... | {
"repo_name": "KaranToor/MA450",
"path": "google-cloud-sdk/platform/gsutil/third_party/boto/tests/integration/rds/test_promote_modify.py",
"copies": "2",
"size": "5397",
"license": "apache-2.0",
"hash": -8197522717942799000,
"line_mean": 38.1086956522,
"line_max": 134,
"alpha_frac": 0.6283120252,
"... |
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